U.S. patent application number 15/457634 was filed with the patent office on 2017-11-30 for systems and methods for the diagnosis and treatment of neurological disorders.
The applicant listed for this patent is Indiana University of Research and Technology Corporation, Rutgers, The State University of New Jersey. Invention is credited to Jorge Jose-Valenzuela, Elizabeth B. Torres.
Application Number | 20170344706 15/457634 |
Document ID | / |
Family ID | 48290675 |
Filed Date | 2017-11-30 |
United States Patent
Application |
20170344706 |
Kind Code |
A1 |
Torres; Elizabeth B. ; et
al. |
November 30, 2017 |
SYSTEMS AND METHODS FOR THE DIAGNOSIS AND TREATMENT OF NEUROLOGICAL
DISORDERS
Abstract
Systems and methods for data compression which facilitate the
diagnosis and treatment of neurodevelopmental and neurodegenerative
disorders. The methods comprise performing the following operations
by a computing device: generating Normalized Data ("ND") from
Original Data ("OD") that defines a Normalized Waveform ("NW") that
is unitless and scaled from zero to one; processing ND to extract
Micro-Movement Data ("MMD") defining a Micro-Movement Waveform
("MMW") comprising a plurality of MMD points; and generating
compressed data comprising a stochastic signature of MMW. Each MMD
point determined based on a value of a peak of NW and a value
representing an average of all data point values between a first
valley of NW immediately preceding the peak and a second valley of
NW immediately following the peak. The stochastic signature is
defined by empirically estimated values of at least one parameter
representing a Probability Distribution Function ("PDF") of a
continuous family of PDFs.
Inventors: |
Torres; Elizabeth B.;
(Piscataway, NJ) ; Jose-Valenzuela; Jorge;
(Bloomington, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Rutgers, The State University of New Jersey
Indiana University of Research and Technology Corporation |
New Brunswick
Indianapolis |
NJ
IN |
US
US |
|
|
Family ID: |
48290675 |
Appl. No.: |
15/457634 |
Filed: |
March 13, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15223884 |
Jul 29, 2016 |
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15457634 |
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14354796 |
Apr 28, 2014 |
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PCT/US12/64805 |
Nov 13, 2012 |
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15223884 |
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62198930 |
Jul 30, 2015 |
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61648359 |
May 17, 2012 |
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61581953 |
Dec 30, 2011 |
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61558957 |
Nov 11, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/4094 20130101;
G16H 40/20 20180101; A61B 5/11 20130101; A61B 5/1125 20130101; H04L
63/0428 20130101; A61B 5/40 20130101; G16H 10/60 20180101; A61B
5/4088 20130101; G16H 20/70 20180101; A61B 5/7264 20130101; A61B
5/7275 20130101; A61B 5/1124 20130101; A61B 5/4848 20130101; A61B
5/162 20130101; G06F 19/00 20130101; A61B 5/0015 20130101; G16H
50/20 20180101; G06F 19/325 20130101; A61B 5/1128 20130101; A61B
5/1114 20130101; A61B 5/4076 20130101; A61B 5/4082 20130101; A61B
5/7278 20130101; A61B 5/1104 20130101; A61B 5/168 20130101 |
International
Class: |
G06F 19/00 20110101
G06F019/00; A61B 5/00 20060101 A61B005/00 |
Goverment Interests
GOVERNMENT RIGHTS
[0002] The invention was made with Government support under Grant
No. 0941587 awarded by the National Science Foundation. The
Government has certain rights in the invention.
Claims
1. A method for data compression, comprising: performing
operations, by a computing device, to generate normalized data from
original data defining neural or bodily rhythms of a subject, the
normalized data defining a normalized waveform that is unitless and
scaled from zero to one; processing, by the computing device, the
normalized data to extract micro-movement data defining a
micro-movement waveform comprising a plurality of micro-movement
data points, each said micro-movement data point determined based
on a value of a peak of the normalized waveform and a value
representing an average of all data point values between a first
valley of the normalized waveform immediately preceding the peak
and a second valley of the normalized waveform immediately
following the peak; and generating, by the computing device,
compressed data comprising a stochastic signature of the
micro-movement waveform, said stochastic signature defined by
empirically estimated values of two parameters representing a
probability distribution function of a continuous family of
probability distribution functions.
2. The method according to claim 1, wherein the original data
comprises sensor data specifying a raw neural or bodily rhythm
created in part by a human subject's physiological system.
3. The method according to claim 1, wherein the normalized data
defines a normalized waveform representing events of interest in a
continuous random process capturing rates of changes in
fluctuations in amplitude and timing of an original raw waveform
defined by the original data.
4. The method according to claim 1, further comprising performing
operations, by the computing device, to estimate moments of a
continuous family of probability distribution functions best
describing a continuous random process.
5. The method according to claim 4, wherein the moments include at
least one of a first moment comprising a mean value, a second
moment comprising a variance value, a third moment comprising
skewness, and a fourth moment comprising kurtosis.
6. The method according to claim 4, wherein the probability
distribution functions comprise a function from a continuous Gamma
family of probability distribution functions.
7. The method according to claim 1, wherein the stochastic
signature is obtained by: performing statistical data binning using
the micro-movement data; processing the binned micro-movement data
to generate a frequency histogram; generating probability
distribution function waveforms using different sets of variable
values; comparing the probability distribution function waveforms
to the frequency histogram to identify a probability distribution
function waveform from the probability distribution function
waveforms that most closely matches a shape and a dispersion of the
frequency histogram; and considering the variable value used for
generating the probability distribution function waveform as the
stochastic signature.
8. The method according to claim 7, wherein vertical columns of the
frequency histogram show how many micro-movement data points are
contained in each of a plurality of statistical data bins.
9. The method according to claim 1, further comprising using the
stochastic signature to obtain at least one of a Noise-to-Signal
Ratio ("NSR") for a signal defined by the original data and a level
of randomness in the original data.
10. The method according to claim 1, further comprising mapping the
stochastic signature on a parameter plane to determine noise and
randomness classifications of a subject's neural or bodily rhythms
defined by the original data.
11. The method according to claim 1, further comprising using the
stochastic signature as a seed value to an encryption algorithm for
encrypting sensitive information prior to being communicated over a
network communications link.
12. The method according to claim 1, further comprising: causing
the computing device or a remote computing device to operate in a
first session state in which first testing operations are performed
to stimulate movement by a human subject in accordance with first
testing parameters; selecting or generating second testing
parameters different from the first testing parameters based on the
stochastic signature; and transitioning the session state of the
computing device or the remote computing device from the first
session state to a second session state in which second testing
operations are performed to stimulate movement by the human subject
in accordance with the second testing parameters.
13. The method according to claim 12, wherein the transitioning is
controlled by the human subject's nervous system evolving with
treatment of a neurological disorder.
14. The method according to claim 1, further comprising receiving
by the computing device the original data which was sent from a
remote device over a network.
15. A system, comprising: a computing device configured to generate
normalized data from original data defining neural or bodily
rhythms of a subject, the normalized data defining a normalized
waveform that is unitless and scaled from zero to one, process the
normalized data to extract micro-movement data defining a
micro-movement waveform comprising a plurality of micro-movement
data points, each said micro-movement data point determined based
on a value of a peak of the normalized waveform and a value
representing an average of all data point values between a first
valley of the normalized waveform immediately preceding the peak
and a second valley of the normalized waveform immediately
following the peak, and generate compressed data comprising a
stochastic signature of the micro-movement waveform, said
stochastic signature defined by empirically estimated values of two
parameters representing a probability distribution function of a
continuous family of probability distribution functions.
16. The system according to claim 15, wherein the original data
comprises sensor data specifying a raw neural or bodily rhythm
created in part by a human subject's physiological system.
17. The system according to claim 15, wherein the normalized data
defines a normalized waveform representing events of interest in a
continuous random process capturing rates of changes in
fluctuations in amplitude and timing of an original raw waveform
defined by the original data.
18. The system according to claim 15, wherein the computing device
is further configured to estimate moments of a continuous family of
probability distribution functions best describing a continuous
random process.
19. The system according to claim 18, wherein the moments include
at least one of a first moment comprising a mean value, a second
moment comprising a variance value, a third moment comprising
skewness, and a fourth moment comprising kurtosis.
20. The system according to claim 18, wherein the probability
distribution functions comprise a function from a continuous Gamma
family of probability distribution functions.
21-32. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Patent Ser. No.
62/198,930 filed on Jul. 30, 2015, and is a continuation-in-part of
U.S. patent application Ser. No. 14/354,796 filed Apr. 28, 2014,
which is a U.S. National Phase of International Patent Application
Serial No. PCT/US2012/064805 filed Nov. 13, 2012, which claims
priority under 35 U.S.C. .sctn.119(e) to U.S. Patent Ser. No.
61/648,359 filed on May 17, 2012, U.S. Patent Ser. No. 61/581,953
filed on Dec. 30, 2011, and U.S. Patent Ser. No. 61/558,957 filed
on Nov. 11, 2011. The content of the above applications are
incorporated by reference in their entirety.
FIELD OF THE INVENTION
[0003] This document relates generally to neurodevelopmental and
neurodegenerative disorders (e.g., autism spectrum disorders). More
particularly, this document relates to systems and methods for
diagnosis and treatment of neurodevelopmental and neurodegenerative
disorders.
BACKGROUND OF THE INVENTION
[0004] Present advancements in genetic and epigenetic research
highlight different sub-types in the Autism Spectrum Disorders
("ASD") of both known and unknown etiological origins. These new
developments pose at least two fundamental challenges: 1)
(Classification): how to distinguish different types of autism
objectively; and 2) (Objective outcome measure): how to treat
different types of autism differently and objectively track
individual cognitive and treatment progress. Current methods are
ineffective at addressing these two objectives.
SUMMARY
[0005] The present document generally relates to implementing
systems and methods for (a) detecting and analyzing a neurological
disorder in a human or animal subject and/or (b) data compression.
In some scenarios, the systems and methods can be used in a medical
context. For example, the systems and methods can be used to
facilitate diagnosis and treatment of neurodevelopmental and
neurodegenerative disorders. The disorders can include, but are not
limited to, Autism Spectral Disorders ("ASD").
[0006] The methods involve performing first operations, by a
computing device, to generate normalized data from original data
(e.g., sensor data specifying a raw neural or bodily rhythm created
in part by a human or animal subject's physiological system). The
normalized data defines a normalized waveform that is unitless and
scaled from zero to one. In some scenarios, the normalized data
defines a normalized waveform representing events of interest in a
continuous random process capturing rates of changes in
fluctuations in amplitude and timing of an original raw waveform
defined by the original data.
[0007] The normalized data is processed by the computing device to
extract micro-movement data. The micro-movement data defines a
micro-movement waveform comprising a plurality of micro-movement
data points. Each micro-movement data point is determined based on
(a) a value of a peak of the normalized waveform and (b) a value
representing an average of all data point values between a first
valley of the normalized waveform immediately preceding the peak
and a second valley of the normalized waveform immediately
following the peak. A peak (local maximum) is automatically
detected as a change in the slope of the curve from positive to
negative (/ \). A valley (local minimum) is automatically detected
as a change in the slope of the curve from negative to positive (\
/).
[0008] Compressed data is generated by the computing device that
comprises a stochastic signature of the micro-movement waveform,
i.e., the entire micro-movement waveform is reduced to the
stochastic signature. The stochastic signature is defined by the
empirically estimated values of two (2) parameters representing a
probability distribution function of a continuous family of
probability distribution functions.
[0009] In some scenarios, the stochastic signature is obtained by:
performing statistical data binning using the micro-movement data;
processing the binned micro-movement data to generate a frequency
histogram; generating probability distribution function waveforms
using different sets of variable values; comparing the probability
distribution function waveforms to the frequency histogram to
identify a probability distribution function waveform from the
probability distribution function waveforms that most closely
matches the shape and the dispersion of the frequency histogram;
and considering the variable value used for generating the
probability distribution function waveform as the stochastic
signature. Vertical columns of the frequency histogram show how
many micro-movement data points are contained in each of a
plurality of statistical data bins.
[0010] In those or other scenarios, the methods also involve
performing operations, by the computing device, to estimate moments
of a continuous family of probability distribution functions best
describing a continuous random process. The moments can include,
but are not limited to, a first moment comprising a mean value, a
second moment comprising a variance value, a third moment
comprising skewness, and/or a fourth moment comprising kurtosis.
Families of probability distribution functions can include, but are
not limited to, the continuous Gamma family of probability
distribution functions.
[0011] The stochastic signature is used to obtain at least one of a
Noise-to-Signal Ratio ("NSR") for a signal defined by the original
data and a level of randomness in the original data. The stochastic
signature is mapped on a parameter plane to determine noise and
randomness classifications of a subject's neural or bodily rhythms
defined by the original data. The stochastic signature may
optionally be used as a seed value to an encryption algorithm for
encrypting sensitive information prior to being communicated over a
network communications link.
[0012] In those or other scenarios, the methods further involve:
causing the computing device or a remote computing device to
operate in a first session state (baseline state) in which first
testing operations are performed to stimulate movement by a human
or animal subject in accordance with first testing parameters;
(subsequent testing steps will be measured relative to the baseline
values and to preceding values, e.g., in a given subsequent step .
. . ) selecting or generating second testing parameters different
from the first testing parameters based on the stochastic signature
(i.e., generating a stochastic trajectory); and transitioning the
session state of the computing device or the remote computing
device from the first session state to a second session state in
which second testing operations are performed to stimulate movement
by the human or animal subject in accordance with the rates of
change from first to second testing parameters (and subsequent
steps). The transitioning is controlled by the human or animal
subject's nervous system evolving with treatment of a neurological
disorder. As such, the rate of change in the therapy/intervention
process is fully driven in parametric form by the individual's
nervous systems' rate of change.
DESCRIPTION OF THE DRAWINGS
[0013] Embodiments will be described with reference to the
following drawing figures, in which like numerals represent like
items throughout the figures, and in which:
[0014] FIG. 1 is an illustration of an exemplary computing device
implementing the present solution.
[0015] FIG. 2 is an illustration of an exemplary system in which
the computing device of FIG. 1 can be employed.
[0016] FIG. 3A provides a flow diagram of (a) an exemplary method
for detecting and analyzing a neurological disorder in a human
subject and (b) an exemplary method for data compression. FIG. 3B
is a continuation of the flow diagram of FIG. 3A.
[0017] FIG. 4A provides a plurality of graphs showing exemplary
sensor data of a representative ASD participant. FIG. 4B provides a
plurality of graphs showing exemplary sensor data of a
representative control subject.
[0018] FIG. 5 provides a graph showing exemplary sensor data and
normalization procedure.
[0019] FIG. 6 provides a graph showing an exemplary micro-movement
waveform extracted from raw movement data.
[0020] FIG. 7 is a graph showing an exemplary histogram
representative of multiplicative random process.
[0021] FIG. 8 is a graph showing an exemplary Gamma waveform fit to
a micro-movements histogram.
[0022] FIG. 9 shows an exemplary classification map.
[0023] FIG. 10 is an illustration of an exemplary reference
map.
[0024] FIGS. 11A, 11B and 11C provides a graph showing tracked
changes in a human subject's stochastic signature as the
intervention is guided by the changes in the subject's nervous
system's output.
[0025] FIG. 12 is a flow diagram of an exemplary method for
selectively and dynamically changing a session state of a computing
system.
[0026] FIGS. 13A, 13B, 13C, 13D, 13E, 13F, 13G, 13H and 13I show
the basic pointing task and examples of representative hand
trajectory and speed profiles. Specifically, FIG. 13A shows a
schematic of the forwards-and-back cycle of pointing behavioral
task. Subjects seat in a chair comfortably in front of a touch
screen. They touch the screen by moving the hand forward towards
the location of a circle, the target presented on a black
background. They retract the hand spontaneously, without
instruction, in a continuous loop forward, towards the circle and
back to rest. The size of the circle was 5 cm. The schematic
trajectories have the location of the global speed maximum in each
segment of the loop. The trials are activated by the screen touch,
so the subjects follow their own comfortable pace and the flow of
motion is continuous. FIGS. 13B, 13C and 13D show the trajectories
in three dimensions for the hand continuously touching the screen
and retracting from it several times in a row. FIGS. 13E, 13F and
13G show the corresponding speed profiles from the instructed
touches and uninstructed retractions. Relevant behavioral landmarks
are highlighted: the target touch (black dot) and the local and
global speed maxima (grey dots). From top to bottom representative
subjects are low-functioning nonverbal; high-functioning verbal;
typical control. FIGS. 13H and 13I provides the temporal speed
profile (zoomed in with the dashed rectangle) for one full
(forward-and-back) motion of LF ASD and from control, respectively.
Notice the presence of kinetic s-Peaks in the LF ASD and absence of
those in the control.
[0027] FIGS. 14A, 14B, 14C and 14D provide s-Peaks
(rastergrams-like) visualization showing temporal micro-dynamics
within a single forward-and-back motion (s-PeaksPeak vector) and
across continuous motion repetitions (s-Peak matrix). More
specifically, FIG. 14A provides a s-PeaksPeak vector: Upper panel
is the hand speed as a function of time in a single forward and
back motion for low functioning ASD subject aligned to the touch
point (set at time=0). The s-Peaks are local and global speed
maxima. Dots mark s-Peaks as the local peaks in the reaching period
toward the target, before the touch or in the retracting period.
Black dots show speed maxima positions in each forward and
retracting cycle. Horizontal time-axis spans from -1,000 ms to
+1,000 ms relative to the touch at time 0. FIGS. 14B, 14C and 14D
show the s-Peak matrix "rastergram" plotted for the three
representative subjects in FIG. 13. Vertical axis is the number of
continuous repeats of full motions (100 in this example) with the
corresponding s-Peaks vector per repeat. They form a s-Peaks
matrix. Each row of the s-Peaks matrix is a s-Peak vector as in
(FIG. 14A). The curve is the averaged speed profile across trials
(used here to highlight the loss of information when averaging.)
The s-Peaks rate per millisecond is calculated every successive 20
frames and shown in the bottom panel.
[0028] FIGS. 15A, 15B, 15C, 15D and 15E provide graphs showing
differences in s-Peaks synchronicity correspond to differences in
spoken language abilities. FIGS. 15A, 15B and 15C show C.sub.(T) as
a function of bin widths .sub.(T) for representative subjects
labeled by their clinical diagnose: (FIG. 15A) Low-functioning;
(FIG. 15B) High-functioning; and (FIG. 15C) control. Insets show
the slopes from the fitted C.sub.(T)-curves. FIG. 15D shows the
averaged C.sub.(T)-trajectories for the ensemble with different
clinical subtypes with computed error bars. For clarity, only early
and late are shown in the C.sub.(T)-trajectories. FIG. 15E shows
bar plots for the second derivate of C(r) for each subgroup. The p
value for comparison between low-functioning and high-functioning
ASD is 0.0011. p=0.0017 between high-functioning ASD and TD adults.
p=0.0001 between low-functioning ASD and TD control. FIG. 15F shows
bar plots for the distance to the C.sub.r(T) curve for each
subgroup. The p value for comparison between low-functioning and
high-functioning ASD is 0.0071. p values for comparison between
high-functioning ASD and TD adults and for comparison between
low-functioning ASD and TD adults are smaller than 0.0001.
[0029] FIGS. 16A, 16B, 16C, 16D, and 16E is a continuation of FIGS.
15A-15E, showing differences in s-Peaks synchronicity according to
population cross-correlation and Fourier analyses. More
specifically, FIG. 16A provides second derivative of the
C(T)-curves and FIG. 16B provides the distance to the total random
curve for each representative subject. Each x-axis position
indicates the clinical label of the subgroup. FIG. 16C-16E provide
fast Fourier transform periodicity--synchronization analyses of
s-Peaks occurrences across the s-Peaks vectors in the matrix
aligned to the touch. FIG. 16C provides autocorrelation of
"s-Spike" chopped vector (bin size 24 frames/104 ms) as a function
of time lag for each subject. Bin size of twenty four (24) frames
is selected according to FIG. 15D, where the slope-based subtypes
were separated in the population cross-correlation. FIG. 16D
provides the power spectrum of s-Spike's chopped vector calculated
from the fast Fourier transform of chopped s-Peaks autocorrelation
(maximum time lag: 30 s). Maximum time lag is limited by the 30
s-time window of the buffering-saving data cycles along the
continuous flow of behavior. FIG. 16E provides the power spectrum
of movement trajectory (along the maximally changing front-back
axis direction of the positional trajectory in the system's world
axes) calculated from fast Fourier transform of the movement's
trajectory autocorrelation (maximum time lag: 30 s).
[0030] FIGS. 17A, 17B, 17C, 17D, 17E and 17F show blind clustering
using the peripheral-inter-peak interval (p-IPI) distribution
analysis in the parameter plane. FIGS. 17A, 17B and 17C provide
frequency histograms of p-IPIs for representative subjects. Upper
panels show combined s-Peaks histogram intervals during reaching
and retracting periods. The histogram's bin size is set as two (2)
frames per eight (8) ms, optimized to produce a clear exponential
fit. Fits are based on p-IPI values below ten (10) frames per forty
(40) ms (in this range all histograms are exponentially
distributed.). Bottom panels in FIGS. 17A, 17B and 17C show
residual p-IPIs histograms outside of the exponential fit (p-IPI
values above 10 frames/40 ms). Insets zoom in the bottom panels to
clearly show that multiple humps of long range p-IPI's present in
control are missing from ASD. The parameter plane is constructed
with the mean p-IPI value and the parameter R from the p-ISI
distribution. FIG. 17D show that three clusters emerged according
to the subject's position on the parameter plane (using K-clusters
method 8). Cluster members were grey shaded according to the three
centroids identified by the algorithm. FIG. 17E provides second
derivative of the C(T) curves and FIG. 17F shows distance to
Poisson reference curve C.sub.r(T) for each subject in each
cluster. Black lines denote the average values in each cluster.
[0031] FIGS. 18A, 18B, 18C, 18D and 18E provide clinical labeling
verification for clustering in FIG. 17 and familial link. More
particularly, FIG. 18A shows points in FIG. 17D are labeled based
on their descriptive diagnosis of verbal-ability (see legend).
Inset shows the center of mass for each subtype (not including the
high-functioning ASD outlier with near zero R). FIG. 18B provides
counts of subjects in each cluster. FIG. 18C provide
characterization of maturation in s-Peaks statistics across typical
and atypical development. Light circles are ASD subjects 10-15
years old; Dark circles are ASD subjects between 16 and 30 years
old; Light stars are 3-4 years old TD children; Dark stars are
control subjects 20-27 years old. FIG. 18D provides the location of
21 parents of the 19 ASD subjects in FIG. 18C. Note the underlying
gray shaded symbols representing the typical young controls. Also
that most parents are located away from the controls, largely
covering the low-to-mid functioning ASD range, along with the
locations of the 3-4 year old TD range in FIG. 18C. FIG. 18E
provides oriented Euclidean distance of 21 parents (coded as in
FIG. 18D) and 8 young adult controls (diamonds) to the centroid of
TD 3-4 year old children on the parameter plane. X-axis is the
difference of mean p-IPI and Y-axis is the oriented Euclidean
distance. A negative value means that the subject is to the left of
the TD 3-4 year old centroid.
[0032] FIGS. 19A, 19B, 19C, 19D, 19E, 19F and 19G provide speed
profile and s-Peaks computations from sensor-collected positional
data for Subject 2 (high-functioning ASD). FIGS. 19A, 19B and 19C
provide hand positions plotted as a function of time in the three
orthogonal directions. Data collected during the same time window
as in FIG. 19C (10 seconds recordings at the sampling rate of 240
frames/s using the Polhemus Liberty system (Polhemus, Colchester,
Vt.)). Black dots denote the target touch located at the position
peaks in the Y direction. FIGS. 19D, 19E and 19F show the velocity
for each direction was calculated from the first derivative of
position with respect to time. The smoothed results obtained from
using the triangulation smoothing algorithm. FIG. 19G provides the
speed magnitude calculated from the smoothed velocity profiles
along the three directions. Dots mark the local and the global
peaks in the speed profile, termed here "peripheral-Spikes"
(s-Peaks-Spikes).
[0033] FIGS. 20A, 20B, 20C, 20D, 20E and 20F provide description of
the smoothing algorithm with window bin selection. FIG. 20A shows
the triangular smoothing algorithm applied using a sliding window
of width 2d+1. Weights were distributed using a symmetrical
triangle as shown in the figure. `Sum` is the sum of weights within
that window. FIGS. 20B and 20C provide triangular and rectangular,
respectively, smoothing applied to an artificial periodic data set
(y=sin(irt/5)+0.10. The smoothing window size varied from 0 to 30.
Note that the triangular smoothing preserves the location of the
peaks whereas the rectangular one does not. FIGS. 20D and 20E show
the smoothing window size effects on the p-IPI distribution
parameters: Mean p-ISI (FIG. 20D) and R parameter (FIG. 20E) for
different representative subjects: control subjects;
high-functioning ASD subjects; mid-functioning ASD subjects; and
low-functioning ASD subjects. The mean p-IPI curves for each
subtype group are plotted in the insets. The window size value was
chosen as 25 frames (104 ms) (dashed line). This corresponds to
different subtypes which are well separated and the R values had
not yet saturated. FIG. 20F shows the speed profile with triangular
smoothing with 25 frames (104 ms) time window, applied to the
velocity vector along each direction and compared to the speed
profile calculated from the raw velocities data.
[0034] FIGS. 21A, 21B, 21C, 21D, 21E, 21F, 21G, 21H, AND 21I
provides a plurality of graphs showing simulation results to
quantify speed smoothness. FIG. 21A shows simulated speed profiles
with SNR=0 dB, 10 dB to 15 dB. Local s-Peaks were marked with green
dots. FIG. 21B shows the corresponding simulated s-Peaks matrix.
FIG. 21C gives the simulated population cross-correlation function
(C.sub.(T)) as a function of bin width (r). The experimental
C.sub.(T) is labeled by connected orange dots. The green dashed
lines are the analytically calculated curves for a random (Poisson)
process having the same firing rate and length as in the trials for
each case. The solid curves are the quadratic polynomials fits to
the C.sub.(T)-curves. FIG. 21D shows the simulated Inter-peak
intervals (IPI) frequency histograms with bin size set at two (2)
frames. Fits were based on s-IPI values below ten (10) frames. FIG.
21E shows relative frequency histograms of residual s-IPIs outside
the exponential fit for the three (3) cases. FIGS. 21F, 21G, 21H,
AND 21I are the parameters calculated for the simulation as a
function of SNR: (F) negative second derivative of the fitted
C.sub.(T) curve with distance of the C.sub.(T) curve (log value),
mean IPI value (log value), and log value of R calculated from
interval frequency histogram. The four (4) parameters calculated
change monotonically with SNR.
[0035] FIGS. 22A, 22B, 22C, 22D, 22E and 22F show FFT analysis of
trajectory and s-Peaks chopped vectors. s-Peaks chopped vector
autocorrelation as in FIG. 19G: FIG. 22A: low functioning; FIG.
22B: high functioning; FIG. 22C: control. FIGS. 22B, 22C and 22D
provide power spectrum of s-Peaks calculated directly from s-Peaks
vector and by calculating FFT of s-Peaks autocorrelation, LF ASD in
FIG. 22D, HF ASD in FIG. 22E, and control in FIG. 22F.
DETAILED DESCRIPTION OF THE INVENTION
[0036] It will be readily understood that the components of the
embodiments as generally described herein and illustrated in the
appended figures could be arranged and designed in a wide variety
of different configurations. Thus, the following more detailed
description of various embodiments, as represented in the figures,
is not intended to limit the scope of the present disclosure, but
is merely representative of various embodiments. While the various
aspects of the embodiments are presented in drawings, the drawings
are not necessarily drawn to scale unless specifically
indicated.
[0037] The present invention may be embodied in other specific
forms without departing from its spirit or essential
characteristics. The described embodiments are to be considered in
all respects only as illustrative and not restrictive. The scope of
the invention is, therefore, indicated by the appended claims
rather than by this detailed description. All changes which come
within the meaning and range of equivalency of the claims are to be
embraced within their scope.
[0038] Reference throughout this specification to features,
advantages, or similar language does not imply that all of the
features and advantages that may be realized with the present
invention should be or are in any single embodiment of the
invention. Rather, language referring to the features and
advantages is understood to mean that a specific feature,
advantage, or characteristic described in connection with an
embodiment is included in at least one embodiment of the present
invention. Thus, discussions of the features and advantages, and
similar language, throughout the specification may, but do not
necessarily, refer to the same embodiment.
[0039] Furthermore, the described features, advantages and
characteristics of the invention may be combined in any suitable
manner in one or more embodiments. One skilled in the relevant art
will recognize, in light of the description herein, that the
invention can be practiced without one or more of the specific
features or advantages of a particular embodiment. In other
instances, additional features and advantages may be recognized in
certain embodiments that may not be present in all embodiments of
the invention.
[0040] Reference throughout this specification to "one embodiment",
"an embodiment", or similar language means that a particular
feature, structure, or characteristic described in connection with
the indicated embodiment is included in at least one embodiment of
the present invention. Thus, the phrases "in one embodiment", "in
an embodiment", and similar language throughout this specification
may, but do not necessarily, all refer to the same embodiment.
[0041] As used in this document, the singular form "a", "an", and
"the" include plural references unless the context clearly dictates
otherwise. Unless defined otherwise, all technical and scientific
terms used herein have the same meanings as commonly understood by
one of ordinary skill in the art. As used in this document, the
term "comprising" means "including, but not limited to".
[0042] As used herein, "Autistic Spectrum Disorder ("ASD") refers
to autism and similar disorders. Examples of ASD include disorders
listed in the Diagnostic and Statistical Manual of Mental Disorders
("DSM-V"). Examples include, without limitation, autistic disorder,
Asperger's disorder, pervasive developmental disorder, childhood
disintegrative disorder, and Rees disorder. Known ASD diagnostic
screenings methods include, without limitation: Modified Checklist
for Autism in Toddlers ("M-CHAT"), the Early Screening of Autistic
Traits Questionnaire, and the First Year Inventory; the M-CHAT and
its predecessor CHAT on children aged 18-30 months, Autism
Diagnostic Interview ("ADI"), Autism Diagnostic Interview-Revised
("ADI-R"), the Autism Diagnostic Observation Schedule ("ADOS") The
Childhood Autism Rating Scale ("CARS"), and combinations thereof.
Known symptoms, impairments, or behaviors associated with ASD
include without limitation: impairment in social interaction,
impairment in social development, impairment with communication,
behavior problems, repetitive behavior, stereotypy, compulsive
behavior, sameness, ritualistic behavior, restricted behavior,
self-injury, unusual response to sensory stimuli, impairment in
emotion, problems with emotional attachment, impaired
communication, and combinations thereof.
[0043] As used herein, "diagnose" refers to detecting and
identifying a disease/disorder in a subject. The term may also
encompass assessing or evaluating the disease/disorder status
(severity, classification, progression, regression, stabilization,
response to treatment, etc.) in a patient. The diagnosis may
include a prognosis of the disease/disorder in the subject. In a
particular embodiment, the diagnosis may determine whether a
subject is a low functioning, mid functioning, high functioning, or
normal individual.
[0044] As used herein, the term "prognosis" refers to providing
information regarding the impact of the presence of a
disease/disorder on a subject's future health (e.g., expected
morbidity or mortality). In other words, the term "prognosis"
refers to providing a prediction of the probable course and outcome
of a disease/disorder or the likelihood of recovery from the
disease/disorder.
[0045] The term "treat" as used herein refers to any type of
treatment that imparts a benefit to a patient afflicted with a
disease, including improvement in the condition of the patient
(e.g., in one or more symptoms), delay in the progression of the
condition, etc.
[0046] The terms "micro-rhythm data" and "micro-movement data", as
used herein, refer to normalized data points (e.g.,
NormPVIndex.sub.1, . . . , NormPVIndex.sub.N). For example, a
micro-movement data point constitutes a single normalized data
point (e.g., the value of NormPVIndex.sub.1). The micro-movement
data defines a micro-movement waveform. The definition of these
terms will become more evident as the discussion progresses.
[0047] The present disclosure concerns systems and methods for
objectively providing a diagnoses, a current state, and/or a
prognosis for a neurological disorder in a human or animal subject.
The neurological disorder can include, but is not limited to, an
autism spectral disorder. The methods involve measuring a raw
bodily rhythm (or motion pattern) of the human or animal subject.
The raw bodily rhythm is measured using sensors coupled to the
human or animal subject. The raw bodily rhythm may be a result of
the performance of typical human or animal functions (e.g., heart
rate, breathing, pumping blood, moving with intent upon
instructions, moving spontaneously without instructions, etc.) or a
result of being stimulated via a visual/auditory/tactile stimulus.
In some scenarios, the stimulus can be provided by a computing
device. The computing device is also referred to herein as an
artificial agent.
[0048] In the artificial agent scenarios, the human or animal
subject is not provided instruction on how to interact with the
artificial agent. The artificial agent provides a stimulus (e.g., a
real-time video of the subject) when the subject contacts a region
of interest (e.g., a virtual region of interest of a displayed
multi-dimensional space). The stimulus is provided to stimulate
movement or motion by the human or animal subject. Data specifying
a raw bodily rhythm or motion pattern of the human or animal
subject's stimulated movement or motion is obtained by the
computing device. This data can include, but is not limited to,
sensor measurement data (e.g., acceleration data, position data,
speed/velocity data, motion sensor data, longitude/latitude data,
height-from-surface data, ElectroCardioGram ("ECG") sensor data,
Respiratory Inductance Plethysmography ("RIP") sensor data). A
difference in the raw bodily rhythm pattern of the subject (e.g.,
particularly fluctuations in the millisecond range) and a standard
raw bodily rhythm pattern (e.g., a previously acquired raw bodily
rhythm pattern of an individual absent of a neurological disorder)
and/or the presence of a raw bodily rhythm pattern associated with
a neurological disorder indicates whether the tested subject has
the neurological disorder. The methods may further involve
measuring other aspects of the human or animal subject (e.g.,
facial patterns) upon the human or animal subject's interaction
with the artificial agent or other stimulus source.
[0049] In some scenarios, the artificial agent is a computing
device displaying dynamic media and/or a Graphical User interface
("GUI") on a display screen. The computing device can include, but
is not limited to, a robot, a three dimensional ("3D") animate, a
personal computer, a laptop computer, a desktop computer, a
personal digital assistant, a smart phone or any other electronic
device having input and output components (e.g., a speaker, a
display screen, a keypad and/or a touch screen). An exemplary
hardware and software architecture for the artificial agent is
discussed in detail below in relation to FIG. 1.
[0050] The present document also concerns systems and methods for
determining an ability of a therapy to modulate (e.g., inhibit or
treat) a neurological disorder (e.g., an autism spectral disorder)
in a human or animal subject. The methods involve: measuring a raw
neural/bodily rhythm pattern of the human or animal subject (e.g.,
fluctuations in the millisecond range) after administering the
therapy (e.g., a pharmaceutical based therapy or a
non-pharmaceutical therapy) to the human or animal subject; and/or
measuring a raw neural/bodily rhythm pattern of the human or animal
subject prior to the administration of the therapy (e.g., as a
baseline). The modulation of the raw neural/bodily rhythm pattern
of the human or animal subject after administration of the therapy
(e.g., to a standard motion pattern) indicates that the therapy
modulates the neurological disorder (e.g., autism spectral
disorder). As previously noted, in some scenarios, these
modulations are measured in the micro-movements extracted from the
raw physiological rhythms. In some scenarios, the neural rhythm
data is obtained from detecting activity in the brain and Central
Nervous System ("CNS"). The neural rhythm data can include, but is
not limited to, local field potential signals, ElectroEncephaloGram
("EEG") data and/or functional Magnetic Resonance Imaging ("fMRI")
data. The bodily rhythm data is obtained from detecting activity in
the Peripheral Nervous System ("PNS").
[0051] The present disclosure further concerns systems and methods
for lessening the improper raw bodily rhythm pattern of a human or
animal subject with a neurological disorder (e.g., an autism
spectral disorder). The methods may involve having the subject
interact with an artificial agent. The artificial agent can include
but is not limited to, a computing device (e.g., a robot and/or
avatar) programmed to encourage the human or animal subject to
react and co-adapt with movement patterns that the artificial agent
is endowed with. The raw bodily rhythm patterns can be gradually
changed so as to objectively reassess the degrees of resistance or
compliance of the human or animal subject's somatosensory
systems.
[0052] Exemplary System Architecture
[0053] Referring now to FIG. 1, there is provided an illustration
of an exemplary computing device 100. The computing system 100 is
generally configured to perform operations for facilitating the
objective diagnosis and treatment of neurodevelopmental and
neurodegenerative disorders. As such, the computing system 100
comprises a plurality of components 102-112. The computing system
100 can include more or less components than those shown in FIG. 1.
However, the components shown are sufficient to disclose an
illustrative embodiment implementing the present invention.
[0054] The hardware architecture of FIG. 1 represents one (1)
embodiment of a representative computing device configured to
facilitate the diagnosis and treatment of neurodevelopmental and
neurodegenerative disorders. As such, the computing system 100
implements methods of the present solution.
[0055] As shown in FIG. 1, the computing system 100 includes a
system interface 112, a user interface 102 (e.g., a keyboard for
data input and a display for data output), a Central Processing
Unit ("CPU") 104, a system bus 106, a memory 108 connected to and
accessible by other portions of the computing system 100 through
system bus 106, and hardware entities 110 connected to system bus
106. At least some of the hardware entities 110 perform actions
involving access to and use of memory 108, which can be a Random
Access Memory ("RAM"), a disk driver and/or a Compact Disc Read
Only Memory ("CD-ROM"). System interface 112 allows the computing
system 100 to communicate directly or indirectly with external
devices (e.g., sensors, servers and client computers).
[0056] In FIG. 1, the computing device 100 comprises sensors 150.
The present solution is not limited in this regard. For example, in
other scenarios, the sensors are separate devices from the
computing device 100. A communications link (wired or wireless) is
provided for enabling communications between the computing device
100 and sensors. In all cases, sensors 150 are coupled to a human
or animal subject for obtaining data from at least one
physiological relevant signal of the subject. The sensor can
include, but is not limited to, an accelerometer, a gyroscope, a
motion sensor, a vibration sensor, a position sensor, a restoration
sensor, and/or a medical sensor (e.g., an electromyography sensor,
an electrocardiogram sensor, an RIP sensor, an MRI sensor,
etc.).
[0057] Hardware entities 110 can include microprocessors,
Application Specific Integrated Circuits ("ASICs") and other
hardware. Hardware entities 110 can include a microprocessor
programmed to facilitate the diagnosis and treatment of
neurodevelopmental and neurodegenerative disorders.
[0058] As shown in FIG. 1, the hardware entities 110 can include a
disk drive unit 116 comprising a computer-readable storage medium
118 on which is stored one or more sets of instructions 114 (e.g.,
software code) configured to implement one or more of the
methodologies, procedures, or functions described herein. The
instructions 114 can also reside, completely or at least partially,
within the memory 108 and/or the CPU 104 during execution thereof
by the computing system 100. The components 108 and 104 also can
constitute machine-readable media. The term "machine-readable
media", as used here, refers to a single medium or multiple media
(e.g., a centralized or distributed database, and/or associated
caches and servers) that store the one or more sets of instructions
114. The term "machine-readable media", as used here, also refers
to any medium that is capable of storing, encoding or carrying a
set of instructions 114 for execution by the computing system 100
and that cause the computing system 100 to perform any one or more
of the methodologies of the present disclosure.
[0059] Notably, the present solution can be implemented in a single
computing device as shown in FIG. 1. The present solution is not
limited in this regard. Alternatively, the present solution can be
implemented in a distributed network system. For example, the
present solution can take advantage of multiple CPU cores over a
distributed network of computing devices in a cloud or cloud-like
environment. The distributed network architecture ensures that the
computing time of the statistics and enhanced functionality is
reduced to a minimum, allowing end-users to perform more queries
and to receive reports at a faster rate. The distributed network
architecture also ensures that the implementing software is ready
for being deployed on an organization's internal servers or on
cloud services in order to take advantage of its scaling abilities
(e.g., request more or less CPU cores dynamically as a function of
the quantity of data to process or the number of parameters to
evaluate).
[0060] Referring now to FIG. 2, there is provided an illustration
of an exemplary system 200. System 200 is a network based system in
which computing device 100 can be deployed in some scenarios. In
this network based scenario, computing device 100 is
communicatively coupled to a server 204 and other computing devices
208.sub.1, . . . , 208.sub.N via a network 202 (e.g., the Internet
or Intranet). Computing devices 208.sub.1, . . . , 208.sub.N can be
the same as, similar to, or different than computing device 100.
During operation, computing devices 100, 208.sub.1, . . . ,
208.sub.N may write data to or read data from database 206. Each
computing device 100, 208.sub.1, . . . , 208.sub.N includes, but is
not limited to, a robot, a three dimensional ("3D") animate, a
personal computer, a laptop computer, a desktop computer, a
personal digital assistant, a smart phone or any other electronic
device having input and output components (e.g., a speaker, a
display screen, a keypad and/or a touch screen). Each of the listed
devices is well known in the art, and therefore will not be
described herein. In some scenarios, the present solution comprises
software that is at least partially installed and run on the
computing device 100, computing device 208.sub.1, . . . , 208.sub.N
and/or server 204.
[0061] Exemplary Methods for Facilitating Diagnosis/Treatment of
Neurological Disorders
[0062] Referring now to FIG. 3, there is provided a flow diagram of
an exemplary method 300 for detecting and analyzing a neurological
disorder in a human or animal subject. Method 300 begins with step
302 and continues with step 304 where at least one sensor (e.g.,
sensor 150 of FIG. 1) is coupled to the human or animal subject for
obtaining data from at least one physiological relevant signal. The
sensor can include, but is not limited to, an accelerometer, a
gyroscope, a motion sensor, a vibration sensor, a position sensor,
a restoration sensor, and/or a medical sensor (e.g., an
electromyography sensor, an electrocardiogram sensor, an RIP
sensor, an MRI sensor, etc.).
[0063] Next in step 306, a first computing device (e.g., computing
device 100 of FIGS. 1-2) collects sensor data generated by the
sensor. The sensor data specifies a raw neural/bodily rhythm
created in part by the human or animal subject's physiological
(e.g., nervous) system. For example, in some scenarios, the sensor
data relates to kinematics motion parameters continuously
registered as a time series of changes in signals generated by the
human or animal subject's nervous system. The raw bodily rhythm can
include, but is not limited to, voluntary bodily rhythms,
involuntary bodily rhythms, and autonomic bodily rhythms. For
example, the raw bodily rhythm defines respiratory rhythms, muscle
rhythms and/or heart beating rhythms. The neural rhythm defines
activity in a subject's brain and/or CNS. The sensor data can be
obtained from a variety of medical tests. The medical tests
include, but are not limited to, an EEG test, an fMRI test, an MRI
test, an ECG test, and/or an RIP test.
[0064] Graphs plotting exemplary sensor data are provided in FIG.
4. In the scenario of FIG. 4, the sensor data comprises samples of
raw head motions extracted from Resting State fMRI ("RS-fMRI")
data, as shown by graphs A1, A2, B1 and B2. Displacement and
rotation kinematics were extracted from raw sensor data using a
Statistical Parametric Mapping ("SPM8") method from raw RS image
files (e.g., files having a Neuroimaging Informatics Technology
Initiative ("NifTI") format) provided in a database (e.g., such as
an Autism Bain Imaging Data Exchange ("ABIDE") database). This
extraction yielded three (3) positional parameters and three (3)
orientation parameters. Graphs A1 and A2 plot representative ASD
participant's linear displacements and angular rotations of
his(her) head registered with respect the a first frame. Graphs B1
and B2 plot representative control subject's linear displacements
and angular rotations of his(her) head registered with respect the
a first frame.
[0065] The sensor data also comprises data defining speed profiles,
as shown by graphs A3, A4, B3 and B4. The speed profiles were
obtained by computing a Euclidean norm of each three dimensional
velocity vector (.DELTA.x, .DELTA.y, .DELTA.z) displacement at each
point of application (x, y, z) from frame to frame. E.g., for three
hundred (300) frames, a speed profile is defined by the following
mathematical equation (1).
speed.sub.frame= {square root over
((.DELTA.x).sup.2+(.DELTA.y).sup.2+(.DELTA.z).sup.2)} (1)
The position data may be filtered using a triangular filter to
preserve the original temporal dynamics of the data (i.e., the
timing of the spike) while smoothing the sharp transitions from
frame to frame.
[0066] Other exemplary sensor data is shown in FIG. 5. The sensor
data comprises data defining the rate of change of a hand's
rotation. As such, the x-axis represents time and the y-axis
represents angular velocity. Accordingly, the scale of the graph's
x-axis is in seconds, and the scale of the graph's y-axis is in
degrees per second. The plotted data points for angular velocity
define an original raw waveform 500. Waveform 500 comprises a
plurality of peaks 502 and a plurality of valleys 504. Each peak
502 is defined by a data point at which the waveform's slope
changes from a positive slope to a negative slope. In contrast,
each valley 504 is defined by a data point at which the waveform's
slope changes from a negative slope to a positive slope.
[0067] Once the sensor data has been collected, the first computing
device optionally encrypts the same so as to comply with at least
the Health Insurance Portability and Accountability Act ("HIPAA")
confidentiality requirements, as shown by step 308. The encryption
is achieved using a chaotic, random or pseudo-random number based
algorithm. Any known or to be known chaotic, random or
pseudo-random number based algorithm can be used herein without
limitation. A seed value for the chaotic, random or pseudo-random
number based algorithm can be selected from a plurality of
pre-defined seed values or dynamically generated during operations
of the first computing device. The term "seed value", as used
herein, refers to a starting value for generating a sequence of
chaotic, random, or pseudo-random integer values. The seed value(s)
can be selected or generated based on the sensor data and/or
information relating to the human or animal subject (e.g., an
identifier, an address, a phone number, an age, a medical
diagnosis, a medical symptom, information contained in a medical
history, a stochastic signature value, a noise signal ratio value,
a moment value, any other value determined in a previous iteration
of method 300, etc.).
[0068] Subsequently, optional step 310 is performed where the
sensor data is communicated over a network (e.g., network 202 of
FIG. 2) from the first computing device to a remote second
computing device (e.g., computing device 208.sub.1, . . . ,
208.sub.N or server 204 of FIG. 2) for storage in a data store
(e.g., memory 108 of FIG. 1 or database 206 of FIG. 2) and
subsequent processing. At the second computing device, the sensor
data may be decrypted if it was previously encrypted by the first
computing device prior to being communicated over the network, as
shown by step 312. Methods for decrypting data are well known in
the art, and therefore will not be described herein. Any known or
to be known decryption technique can be used herein without
limitation.
[0069] The second computing device also performs operations to
normalize the sensor data, as shown by step 314. This step is very
important when dealing with parameters of different units and
scales. The normalization is performed to obtain normalized data
defining a normalized waveform that is unit less and scaled from
zero (0) to one (1). The normalized waveform represents events of
interest in a continuous random process capturing rates of changes
in fluctuations in amplitude and timing of an original raw waveform
(e.g., waveform 500 of FIG. 5) defined by the sensor data (e.g.,
sensor data 500 of FIG. 5). Methods for normalizing data are well
known in the art. Any known or to be known data normalizing method
can be used herein without limitation.
[0070] In more general terms, the normalization is performed to
standardize the different resolutions and/or scales/units of the
time series waveforms defined by the sensor data. For example, a
heart rate waveform has a millisecond scale. A velocity waveform
has a centimeter per second scale. An acceleration waveform has a
meter per second squared scale. The different units of these
waveforms are standardized in a waveform which is normalized from
zero (0) to one (1).
[0071] In some scenarios, the sensor data normalization is achieved
using the Euclidean distance so that all parameters have the same
scale. The following mathematical equation (2) is used to implement
a unity-based normalization.
X i , 0 to 1 = X i - X Min X Max - X Min ( 2 ) ##EQU00001##
where X.sub.i represents each data point i, X.sub.MIN represents
the minima among all the data points, X.sub.MAX represents the
maxima among all the data points, X.sub.i, owl represents the data
point i normalization between zero (0) and one (1). Alternatively,
the following mathematical equation (3) can be used to produce a
set of normalized data with zero (0) being the central point.
X i , - 1 to 1 = X i - ( X Max + X Min 2 ) ( X Max - X Min 2 ) ( 3
) ##EQU00002##
where X.sub.i represents each data point i, X.sub.MIN represents
the minima among all the data points, X.sub.MAX represents the
maxima among all the data points, X.sub.i, _tot represents the data
point i normalization between zero (0) and one (1).
[0072] In other scenarios, the sensor data normalization is
achieved using the following mathematical equation (4).
NormPVIndex = SpeedMax SpeedMax + AvrgSpeed ( 4 ) ##EQU00003##
[0073] where NormPVIndex represents a normalized data point,
SpeedMax represents a value of a peak (e.g., peak 500 of FIG. 5),
and AvrgSpeed represents an average of all data point value between
a first valley (e.g., valley 504A of FIG. 5) immediately preceeding
the peak and a second valley (e.g., valley 504B of FIG. 5)
immediately following the peak.
[0074] In a next step 315, the second computing device processes
the normalized data to extract micro-rhythm data or micro-movement
data defining a micro-rhythm or movement waveform. The terms
"micro-rhythm data" and "micro-movement data", as used herein,
refer to normalized data points (e.g., NormPVIndex.sub.1, . . . ,
NormPVIndex.sub.N). For example, a micro-movement data point
constitutes a single normalized data point (e.g., the value of
NormPVIndex.sub.1). The micro-movement data defines a
micro-movement waveform. An exemplary micro-movement waveform 600
is shown in FIG. 6.
[0075] Upon completing step 315, step 316 is performed where second
computing device estimates (a) a stochastic signature of the
micro-movement waveform and (b) moments of a continuous family of
probability distribution functions best describing the continuous
random process. The probability distribution function comprises a
Gamma function, a Gaussian Distribution function, and/or a Log
Normal Distribution function. Each of these functions is well known
in the art, and therefore will not be described in detail herein.
Any known or to be known Gamma, Gaussian Distribution and/or Log
Normal Distribution function can be used herein without
limitation.
[0076] In some scenarios, the stochastic signature estimation is
obtained by: performing statistical data binning using the
micro-movement data; processing the binned micro-movement data to
generate a frequency histogram; and performing a Maximum Likelihood
Estimation ("MLE") process using the frequency histogram to obtain
the stochastic signature. The MLE process generally involves:
generating probability distribution function waveforms using
different sets of variable values; comparing the probability
distribution function waveforms to the frequency histogram to
identify a probability distribution function waveform from the
probability distribution function waveforms that most closely
matches the shape of the frequency histogram; and considering the
variable value used for generating the probability distribution
function waveform as the stochastic signature.
[0077] Techniques for statistical data binning are well known in
the art, and therefore will not be described in detail here.
However, it should be understood that in some scenarios the data
binning generally involves grouping each set of micro-movement data
points in respective bins, where micro-movement data points of each
set have the same value (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,
0.8, 0.9 or 1.0) or fall within a specified range of values (e.g.,
0.0-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, . . . , 0.9-1.0).
[0078] The binned data is used to generate a frequency table
specifying the frequency of micro-movement data points in each bin
(or stated differently, the total number of micro-movement data
points in each bin). An exemplary frequency table is shown
below.
TABLE-US-00001 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 4 7 14 14 11
9 11 8 8 6
The frequency table is then used to generate the frequency
histogram.
[0079] The frequency histogram is constructed from the frequency
table. The intervals from the frequency table are placed on the
x-axis and the values needed for the frequencies are represented on
the y-axis. In effect, the vertical columns of the frequency
histogram show how many micro-movement data points are contained in
each bin. An exemplary frequency histogram 700 is provided in FIG.
7.
[0080] The frequency histogram is then used in the MLE process to
obtain an estimated stochastic signature. The MLE process involves
estimating a mean value and a variance value while only knowing a
relatively small number of sensed micro-rhythms or micro-movements
of the human or animal subject. The MLE process accomplishes this
by: generating probability distribution function waveforms using
different sets of mean and variance values; and comparing the
probability distribution function waveforms to the frequency
histogram to identify the probability distribution function
waveform that most closely matches the shape of the frequency
histogram.
[0081] In some scenarios, a Gamma function is used to generate the
probability distribution function waveforms. The Gamma function is
defined by the following mathematical equation (5).
.gamma. = f ( x a , b ) = 1 b a .GAMMA. ( a ) x a - 1 e - x b ( 5 )
##EQU00004##
where a is the shape parameter, b is the scale parameter, and y is
the Gamma function result. Different sets of values for a and b are
used to generate a plurality of Gamma function waveforms which are
compared to the frequency histogram. The a and b values associated
with the Gamma function waveform that most closely matches the
shape of the frequency histogram define the stochastic signature
for the human or animal subject.
[0082] An exemplary Gamma function waveform 800 generated using
mathematical equation (5) is shown in FIG. 8. The Gamma function
waveform 800 is over-laid on top of the histogram 700 of FIG. 7. As
can be seen from FIG. 8, the shape of Gamma function waveform 800
closely matches the shape of histogram 700. As such, the a and b
values used as inputs to mathematical equation (5) define the
stochastic signature for the human or animal subject.
[0083] As noted above, step 316 also involves estimating the
moments of a continuous family of probability distribution
functions best describing the continuous random process. In the
Gamma function scenarios, two moments are estimated. A first
estimated moment comprises a mean value p defined by the following
mathematical equation (6).
.mu.=a.times.b (6)
[0084] A second estimated moment comprises a variance value defined
by the following mathematical equation (7).
.sigma.=a.times.b.sup.2 (7)
[0085] Referring again to FIG. 3A, method 300 continues with step
317. In step 317, the second computing device performs operations
to obtain (a) a Noise-to-Signal Ratio ("NSR") to track levels of
noise in the sensor data and (b) a level of randomness in the
underlying raw data from the estimated stochastic signature and/or
moments. The NSR is defined by the following mathematical equation
(8).
NSR=.sigma./.mu. (8)
In the Gamma scenarios, the above mathematical equation (8) can be
re-written as the following mathematical equation (9).
NSR=(a.times.b.sup.2)/(a.times.b)=b (9)
As evident from mathematical equation (9) the NSR is equal to the
scale parameter b in the Gamma scenarios. The level of randomness
L.sub.random is defined by the following mathematical equation
(10).
L.sub.random=a (10)
[0086] In a next step 318, the second computing device maps the
stochastic signature on a parameter plane and/or on a space spanned
by the estimated moments so as to generate a classification map. In
the Gamma scenario, this mapping involves plotting a data point on
a graph having an x-axis with numbers representing a shape
parameter a of the Gamma function and a y-axis with numbers
representing a scale parameter b of the Gamma function, where the
Gamma function generates a waveform that most closely matched the
frequency histogram. A schematic illustration of an exemplary
classification map 900 (or map) is provided in FIG. 9.
[0087] After generating the classification map, step 319 is
performed where the human or animal subject is localized relative
to other human or animal subjects (including individuals without a
neurological disorder and individuals with a neurological disorder
having a pathology of known origins). This localization is achieved
using a classification map. The result of the localization is a
classification of whether a human or animal subject's neural/bodily
rhythms are noisy and random, and therefore unpredictable. This
classification is useful for diagnosis of a neurological
disorder.
[0088] In some scenarios, the classification map comprises
classification map 900. As noted above, the NSR is equal to the
scale parameter b of the Gamma function. Therefore, along the
y-axis of the clarification map 900, the higher the value the
higher the noise in a signal (or stated differently, the lower the
value on the value on the y-axis the higher the signal). So, the
amount of noise in the random process can be quantified. Along the
x-axis of the classification map 900, the closer to one (1) the
more random the process and less informative the collected data is
of future events (the shape value of one corresponds to the
memoryless Exponential distribution, the most random probability
distribution function there is). In contrast, the farther to the
right from the shape value of one the more predictive the collected
data is of future events. The mapping provides a means to classify
for diagnosis purposes whether a human or animal subject's
neural/bodily rhythms are noisy and random, and therefore
unpredictable. A classification of noise and random neural/bodily
rhythms indicates that the human or animal subject has a
neurological disorder.
[0089] As noted above, the result of the localization is a
classification of whether a human or animal subject's neural/bodily
rhythms are noisy and random, and therefore unpredictable. This
classification is useful for the objective diagnosis of a
neurological disorder. The classification result of the
localization can be used to track progress or improvement in the
subject's neurological disorder if there is a concurrent
therapy.
[0090] Therefore upon completing step 319, method 300 continues
with optional step 320 of FIG. 3B. In optional step 320, changes in
the human or animal subject's stochastic signature are tracked over
a period of time (e.g., hours, days, weeks, months, years). A graph
showing tracked changes in a human or animal subject's stochastic
signature is provided in FIG. 11. Such tracking provides a means to
detect positive or negative progression of a neurological disorder,
i.e., whether the neurological disorder is getting worse or whether
there has been an improvement of the neurological disorder.
[0091] In a next step 321, the second computing device performs
operations to confirm whether or not the human or animal subject
has a neurological disorder and/or to determine the type of
neurological disorder. This confirmation/determination is achieved
using a reference map comprising data points associated with a
plurality of reference individuals having an neurological disorder
and a plurality of reference individuals without a neurological
disorder. The reference map is pre-stored in a data store (e.g.,
memory 108 of FIG. 1 and/or data store 206 of FIG. 2) that is
accessible to the second computing device.
[0092] An exemplary reference map 1000 is shown in FIG. 10. The
reference map 1000 comprises a plurality of data points 1002 (e.g.,
the red data points) associated with individuals without a
neurological order, a plurality of data points 1004 (e.g., the blue
data points) associated with individuals having schizophrenia, and
a plurality of data points 1006 (e.g., the purple data points)
associated with individuals having ASD. The stochastic signature
values for the human subject are processed to obtain a log scale
value and a log shape value. The log scale and log shape values are
then plotted on the reference map 1000. The location of the data
point associated with the human subject indicates (1) whether the
human subject has a neurological disorder, (2) the type of
neurological disorder, and/or (3) possible causes of the
neurological disorder (e.g., deletion of a chromosome). For
example, if the data point associated with the human subject falls
in the top left corner of reference map 1000, then a diagnosis is
made that the human subject has ASD with possible causes defined by
the known origins of ASD in the reference individuals with similar
NSRs, level of randomness, and stochastic signatures. In contrast,
if the data point associated with the human subject falls in the
bottom right corner of reference map 1000, then a determination is
made that the human subject does not have a neurological
disorder.
[0093] The results of previous steps 319-321 are then used by the
second computing device to generate diagnosis information to be
communicated to an entity testing the human or animal subject
(e.g., a physiologist), as shown by step 322. The diagnosis
information specifies (a) noise/randomness classification of the
human or animal subject's neural/bodily rhythm, (b) whether the
human subject has a neurological disorder, (c) the type of
neurological disorder, (d) the progression of the human or animal
subject's neurological disorder, and/or (e) possible causes of the
human or animal subject's neurological disorder.
[0094] In some scenarios, the second computing device encrypts the
diagnosis information as shown by optional step 323. The encryption
is achieved in accordance with a chaotic, random or pseudo-random
based algorithm for generating a numerical sequence. Any known or
to be known chaotic, random or pseudo-random number based algorithm
can be used herein without limitation. A seed value for the
chaotic, random or pseudo-random number based algorithm can be
selected from a plurality of pre-defined seed values or dynamically
generated during operations of the second computing device. The
seed value(s) can be selected from or generated based on the sensor
data and/or information relating to the human or animal subject
(e.g., an identifier, an address, a phone number, an age, a medical
diagnosis, a medical symptom, information contained in a medical
history, the estimated stochastic signature, a mean value, a
variance value, an NSR value, a level of randomness value, a value
indicating a positive or negative change in the stochastic
signature, moment values, etc.). Upon completing step 322 and/or
323, step 324 is performed where the diagnosis information is
communicated from the second computing device to the first
computing device via the network.
[0095] At the first computing device optional step 326 may be
performed. Optional step 326 involves decrypting the diagnosis
information at the first computing device. Techniques for
decrypting data are well known in the art, and therefore will not
be described herein. Any known or to be known decryption technique
can be used herein without limitation.
[0096] In a next step 328, the first computing device performs
operations to present the diagnosis information to a user thereof.
The diagnosis information can be presented via a display, a
speaker, or other output device of the first computing device. The
diagnosis information can be presented to the user in any auditory
format, visual format (e.g., a textual format, a graphical format,
a table format and/or a chart format), and/or tactile format (e.g.,
as vibration). The diagnosis information can be used to select a
treatment plan that is appropriate and is likely to be most
effective for the human or animal subject, and which has had a
history of improving the same neurological disorder in other
individuals with similar or the same test results (e.g., stochastic
signatures).
[0097] In some scenarios, the method 300 continues with optional
steps 330 where another iteration of method 300 may be performed.
Subsequent to completing step 330, step 332 is performed. In step
332, method 300 ends or other processing is performed.
[0098] Notably, at least steps 306-316 provide a technique to
compress sensor data. In this regard, the sensor data collected in
step 306 is encoded using fewer bits than the original
representation. The encoding is achieved by generating compressed
data comprising a stochastic signature of neural/bodily rhythms
defined by the sensor data. Decompression is possible in this case
since the stochastic signature can be used as an input to a
probability distribution function (e.g., a Gamma function, a
Gaussian Distribution function, and/or a Log Normal Distribution
function) so as to produce a waveform that closely matches a
frequency histogram of micro-movement waveform data point values.
The data compression technique can also include steps 317-319 in
some cases.
[0099] Referring now to FIG. 12, there is provided a flow diagram
of an exemplary method 1200 for selectively and dynamically
changing a session state of a computing system (e.g., computing
device 100 of FIG. 1 and/or system 200 of FIG. 2) based on a human
or animal subject's stochastic signature(s). The session state
change is driven in real-time by a human or animal subject's
nervous system evolving with treatment of a neurological disorder.
The session state change of the computing system facilitates the
tailoring of neurological disorder testing based on the human or
animal subject's nervous system's status.
[0100] As shown in FIG. 12, method 1200 begins with step 1202 and
continues with step 1204 where a software application (e.g.,
instructions 114 of FIG. 1) installed on a computing device (e.g.,
computing device 100 of FIG. 1) is launched. In a next step 1206,
the computing device receives a user software interaction to
initiate a first test for determining whether a human or animal
subject has a neurological disorder. In response to the first
test's initiation, step 1208 is performed where testing parameters
of the computing device are set to default values. As a result of
using default values for the testing parameters, the computing
device is transitioned to a first session state in which default
testing operations are performed. The testing parameters can
include, but are not limited to, parameters for causing a
particular visual, auditory and/or tactile stimulus of a plurality
of stimuli to be output, parameters selecting one or more
probability distribution functions (e.g., a Gamma function, a
Gaussian Distribution function, and/or a Log Normal Distribution
function), parameters selecting a particular data binning method
from a plurality of statistical data binning methods, parameters
selecting a cryptographic algorithm from a plurality of
cryptographic algorithms, parameters selecting seed values for a
given cryptographic algorithm, and/or parameters selecting a sensor
data normalization algorithm.
[0101] Upon completing step 1208, step 1210 is performed where the
computing device performs operations to output a stimulus therefrom
for stimulating neural/bodily rhythms by the human or animal
subject. The stimulus can include a visual component, an auditory
component and/or a tactile component. The computing device then
obtains sensor data specifying sensed neural/bodily rhythms, as
shown by step 1212. Thereafter, step 1214 is performed where
operations are performed by a computing system (e.g., computing
system 200 of FIG. 2) to obtain various information. This
information includes, but is not limited to, (a) a stochastic
signature of a micro-movement waveform, (b) the moments of a
continuous family of probability distribution functions best
describing the continuous random process, (c) an NSR indicating a
level of noise in sensor data, and/or (d) the level of randomness
in underlying raw data. Such information can be obtained by
performing method 300 discussed above. As such, step 1214 involves
performing some or all of the method steps discussed above in
relation to FIGS. 3A-3B.
[0102] Subsequent to completing step 1214, step 1216 is performed
where the computing device and/or computing system dynamically
select or generate new values for the testing parameters of the
computing device based on the information obtained in previous step
1214. For example, the new values can be selected or generated
based on a stochastic signature of the human or animal subject. The
stochastic signature values can be used as inputs into an algorithm
for generating new testing parameters. Additionally or
alternatively, one or more rules can be defined which indicate
which values of a plurality of pre-defined values should be used as
the new values when the stochastic signature values match
pre-specified values.
[0103] The non-stationary nature of the nervous systems signals
permits the tracking of the rate of change in the empirically
estimated stochastic parameters forming a stochastic trajectory. As
these empirically estimated parameters evolve (e.g., see FIG. 11
panels B-C), it is possible to obtain the frequency and amplitude
of stochastic shifts on the Gamma parameter plane from the
quadrants of the Gamma parameter plane delimiting the left upper
quadrant of high NSR and randomness vs. the right lower quadrant of
low NSR and predictable statistics. Such limits are automatically
determined by obtaining the median values of the empirically
obtained shape and scape parameters across the stochastic
trajectory. Furthermore, stochastic rules, such as that derived in
attached reference from the Behavioral and Brain Functions, permit
the prediction of future values of speed and acceleration
self-generated by the nervous system of the person, based on
previously experienced (sensed) values in the continuous stream of
micro-movements. The micro-movements scaled between zero (0) and
one (1) are mapped to the actual physical values and corresponding
units from the sensors in use (e.g., cm/s, m/s.sup.2, deg/ms,
.mu.V, etc.). This permits the assessment of boundaries of change
for each individual directly obtained as a read-out of the nervous
system of the person in reaction to therapy (pharmacological and/or
behavioral). As such, any changes in analytical parameters are
informed and directly driven by the changes in nervous system's
output and by their actual rates of change. Of note, as the changes
in the signal and empirical estimated parameters reach stability
(e.g., stationary changes in the right lower quadrant of low NSR
and high predictability), the nervous system's output indicates
that learning and adaptation has taken place. As such the
effectiveness of the therapy (or of motor learning in general,
e.g., sports training as in the Behavior and Brain Functions paper)
is objectively assessed through this outcome measure method.
[0104] Once the new values have been selected or generated, the
testing parameters are set by the computing device to the new
values, as shown by step 1218. In effect, the session state of the
computing device is transitioned from a first session state to a
second session state in which tailored testing operations are
performed. The tailored testing operations ensure that the most
effective form of sensory-motor feedback is employed when
re-testing the human or animal subject. The term "sensory-motor
feedback", as used herein, refers to the process performed by a
human or animal that involves taking sensory information and using
it to make motor actions. Included in the sensory input is the
kinesthetic feedback, referred to as kinesthetic reafference, in
this case, the self-generated micro-movements sensed back through
peripheral afferent nerves. In some scenarios, the most effective
form of motor-driven-sensory feedback is one that causes the human
or animal subject's neural/bodily rhythms to converge towards
neural/bodily rhythms of an individual without any neural disorder,
i.e., those empirically quantified thus far in over 500
typical/healthy young individuals on the right lower quadrant of
the Gamma parameter plane.
[0105] When the computing device is in its second session state,
step 1220 is performed where the computing device performs
operations to obtain certain information. This information
includes, but is not limited to, (a) a stochastic signature of a
micro-movement waveform, (b) the moments of a continuous family of
probability distribution functions best describing the continuous
random process, (c) an NSR indicating a level of noise in sensor
data, and/or (d) the level of randomness in underlying raw data.
Such information can be obtained by performing method 300 discussed
above. As such, step 1220 involves performing some or all of the
method steps discussed above in relation to FIGS. 3A-3B.
Subsequently, step 1222 is performed where method 1200 ends or
other processing is performed.
[0106] Exemplary Studies and Results Via Implementations of the
Present Solution
[0107] ASD affects over one percent (1%) of school-age children. A
current challenge for diagnosis based on observation is the highly
heterogeneous behavioral presentation that impedes sub-typing the
disorder according to severity. Behaviors being observed and scored
involve highly variable movements with different levels of intent.
Here, the temporal dynamics of the motor output variability from
continuously flowing movements are studied at a much finer level
involving millisecond time scales. The hand speed trajectories are
very irregular. At a micro-level, they have millisecond-range peak
fluctuations that are termed peripheral speed spikes
(s-Peaks-Spikes). In fifty-nine (59) subjects (19/30 ASD with their
family too), the s-Peaks-Spikes' synchronicity and periodicity
patterns were examined. To this end, the above described methods
were employed and augmented with new indexes to be explained next
so as to first perform analytical simulations, create various
possible scenarios and then test and validate the analytically
obtained parameters with the actual empirical data. The empirically
obtained statistics' parameters (well informed by the analytical
simulations) sub-typed autism severity and revealed that 13/21
parents clustered together with their affected child, indicating a
putative genetic link. These s-Peaks--previously considered just
noise and traditionally smoothed out--contain important information
providing new quantitative biomarkers to objectively classify ASD
subtypes.
[0108] Movement abnormalities are not a core symptom of ASD, yet
movement is implicitly present in each of the symptoms currently
defining the disorder. Movements provide a new tool to assess ASD
and to track cognitive and behavioral changes because the same
brain that controls cognitive processes controls body movements.
Cognition and movement coexist in a closed loop where one component
impacts the other. There are two main types of cognitive processes:
deliberate-conscious and automatic-unconscious (Gazzaniga, M. S.,
The Cognitive Neurosciences. 4th Ed., (MIT Press, 2009)). Current
methods in cognitive psychology cannot access the unconscious
mental processes because they rely on inferences and verbal reports
about mental operations and perceptions that reach
consciousness.
[0109] Movements also come in these two flavors, along with a range
of flavors between these two (2) limiting cases. Some movements are
deliberate, generally aimed at a goal and consciously controlled.
Deliberate movements stand in contrast to spontaneous movements.
Spontaneous movements are automatically carried along by the body
in transition to other goal-directed movements and tend to be
largely below conscious awareness. They do not permeate with the
same level of intentionality or goal-directness as deliberate
movements. The trajectories of spontaneous movements change as a
function of the speed whereas those of deliberate movements keep
their intended course despite speed changes. In natural
motions--whether instrumental activities of daily living or complex
choreographic sequences--the ebb and flow of these two movement
classes can be unambiguously identified with the random
fluctuations of movement parameters and connected with the ebb and
flow of mental processes. Unlike automatic cognitive processes that
cannot be reported on, spontaneous movements that also occur
largely below conscious awareness can be precisely and objectively
quantified, independent of any inferences. They also provide a new
tool to connect body movements and cognitive abilities. The random
fluctuations--particularly in the millisecond range--of trajectory
parameters during deliberate and automatic pointing movements--such
as the hand speed maximum and the time to attain the speed
maximum--give away unequivocal differences between those with
neurodevelopmental and neurodegenerative disorders (e.g.,
individuals with ASD) and their typically developing (TD) peers,
optionally in the naturalistic settings of their classroom
environment.
[0110] The dichotomy between deliberate and spontaneous motions is
new, yet it is present in cognitive processes, body gestures for
communication, biological motions, facial expressions, ON/OFF
states of saccadic eye movements, speech, etc. Most likely the core
(more primitive) automatic centers of the brain control the
spontaneous motions (e.g., the brain stem, the cerebellum, basal
ganglia and limbic system). These centers are severely disrupted
during development in ASD. In certain individuals, they may not
mature and give rise to the goal-directness necessary to leave the
autistic bubble and explore the peripersonal space, much less the
social medium. However, it is possible to tap into mental processes
that are below conscious awareness and objectively measure the
behavioral outcome in the unconscious movements. This is because
the micro-movements' waveform reflect a readout of the nervous
system's self-generated actions, under the voluntary, automatic and
spontaneous control.
[0111] In cognitive psychology and cognitive neuroscience there are
two separate cognitive systems--those that control automatic
processes and those that control more deliberate ones. The two
classes of movement can connect with these two cognitive
categories--automatic and deliberate. When cognitive processes and
movement mechanisms are studied in a close loop such that cognition
impacts movement and movement impacts cognition, one can be
influenced by reshaping the other. So one can make automatic
cognitive processes impact spontaneous motions and deliberate
cognitive processes impact deliberate motions. In the case of
low-functioning non-verbal children with ASD one can very precisely
and objectively track their spontaneous learning with no
instructions or specific goals.
[0112] Herein, cognition and movement have been connected and this
connection has been made objectively quantifiable. These objective
measurements may be performed in about fifteen (15) minutes, with
minimal disruption of daily routines. Notably, the task, the
stimuli, the medication, etc. can be changed and the outcome very
precisely measured before and after the manipulation to assess
performance gains. Each child has different sensory preferences and
capabilities, and different predispositions to learn. These can be
measured and personalized target therapies may be identified and
tailored according to the neurodevelopmental and neurodegenerative
disorder or ASD type. Accordingly, the instant invention has
provided an entirely objective metric of behavioral performance
that is simple to use, fast to acquire, and mathematically
sound.
[0113] While the instant invention has been exemplified with ASD,
the methods of the instant invention can be used with other
developmental/mental disabilities or neurological disorders. For
example, the methods of the instant invention can be used in
sports, perceptual sciences, and the performing arts. Examples of
developmental/mental disabilities or neurological disorders that
the instant methods can be used with include, without limitation,
attention deficit hyperactivity disorder (ADHD), Parkinson's
Disease, stroke (e.g., stroke in the cortex, particularly the
posterior parietal cortex; Tones et al. (2010) J. Neurophysiol,
104:2375-2388), Down syndrome, William syndrome, schizophrenics,
concussive injuries (e.g., sports concussion), and the like. The
instant methods may also be used with other developmental
disabilities to identify traits solely associated with ASD with
clearly localized regions of the Gamma-plane in relation to other
disorders that also impact mental capabilities. Stochastic rates of
change and rules may be identified that are specific to ASD.
[0114] In a particular embodiment of the instant invention,
real-time hand movements are captured from the subject (e.g.,
animal, particularly human (e.g., child) as he/she faces an
interface (e.g. a computer monitor) and are used to trigger
real-time videos (e.g., videos of self from a camera facing the
subject, pre-recorded cartoon videos of the subject's preference,
or the like). The videos are triggered by the subject's real-time
motions of his/her hand when the motions are constrained to a
virtual Region Of Interest ("vROI") defined by the experimenter. In
a particular embodiment, the subject discovers this vROI on his/her
own, without instructions. The self-discovery of the vROI emerges
by random exploratory motions of the hand. Once the subject
discovers the motion/vROI that triggers the videos, they
systematically initiate motions that will sustain the video ON or
that will flicker the videos successively ON/OFF. Typically
developing subjects (TD) verbally communicate the succession of
events to the experimenter. They undergo an "aha!" moment that they
tend to want to explain to the experimenter. In the cases of the
subject with autism spectrum disorders, the progression is
different because they have various degrees of functionality that
range from very low-functioning non-verbal to high-functioning
verbal abilities. However, they manifest changes in their motion
patterns and facial expressions which are objectively quantified.
These indicate substantial changes in engagement with the task and
reveal the progression of the task. Their motions reveal how to
attain the goal of reaching the specific vROI in space in order to
attain their reward (e.g., videos of themselves or prerecorded
cartoons of their preference). Without instructions, they can then
proceed to control the flow of the video display as a function of
the reshaping of their personal stochastic signatures of movement
variability (the random fluctuations in the movement patterns that
are characteristic to each individual subject). The metrics reveal
which form of reward is most effective in the sense of engaging the
subject in the active exploration of the environment and exiting
for a moment their "autistic bubble". Sessions typically last a few
minutes and enable measurement behavior before and after this
exploratory exercise.
[0115] In a particular embodiment, the method of the instant
invention is a real-time co-adaptive interface between the subject
and an artificial agent (e.g., a robot, three dimensional animate
(e.g., avatar), screen, or any dynamics media with controllable
dynamics). As above, there may be no explicit instructions or goals
given to the subject. The user rather may discover the goal through
random behavior that leads to active exploration of its
surroundings stimulated by a reward (some change in the external
stimulus). Once the exploration turns systematic, the subject can
discover that it can act in tandem with the media's dynamics and
control it. This sensory-feedback based control in closed loop with
the external media (agent) can then be used to co-adapt the agent's
and the user's motions, emotions, etc. The external media with
dynamics can be a robot, a virtual three dimensional animate, sound
(speech) or any kind of simpler sensory input that changes over
time with some structure (i.e., that has dynamics).
[0116] As stated hereinabove, this methodology may lack
instructions or well defined goals. Unlike other methods that also
exploit sensory-motor feedback in closed loop (e.g. the Wii.RTM.,
brain machine interfaces, etc.) the instant methodology does not
require the subject to understand a priori what the goal of the
task should be. The subject comes to realize the goal by randomly
interacting with the environment and discovering the contingencies
that evoke a rewarding experience. Since methods have been
developed that can objectively quantify the subject's sensory
preferences in the stochastic patterns of his motions, one can
identify in real time which reward is the most effective in
engaging the subject and fostering systematic exploratory behavior
that potentially can lead to active control of the stimulus in
closed loop. Once the active control of the stimulus is attained,
one can co-adapt the motions of the subject and those of the
stimulus in real time. This means that one can reshape the motions
of the subject by reshaping the stimulus motions. One can reshape
the stimulus motions in ways that can shift the stochastic
signatures of the autistic subject towards typical patterns that
are acceptable within social settings without having to explicitly
tell the subject. Since the random fluctuations of
velocity-dependent parameters are a readout of the subject's
somatosensation, it is possible to very precisely quantify if there
is resistance or compliance to co-adaptation under specific types
of noise that the external media can be endowed with. In other
words, a form of augmented sensory feedback has been created that
can be precisely parameterized and its effectiveness tracked in
real time.
[0117] The stimulus, which may be real time self-videos or videos
of the subject's preference, can be replaced by other media (e.g.,
audio, touch-vibration-type stimuli, etc.) including an
anthropomorphic robot that the motions of the subject can control
in closed loop with the motions of the robot. The stimulus can also
be an animated three dimensional animate embedded in a social
environment so one can co-adapt the subject and the three
dimensional animate within a social setting to build and to foster
theory of mind.
[0118] As stated hereinabove, the methods of the instant invention
may be performed with a robot or robotic interface. Robots provide
an amenable platform to teach the subject because they cannot only
move autonomously but respond to the subject's movements as well.
Robots can detect different motion patterns in different subject
populations and be trained to move with specific statistical
signatures of variability that may be more appropriate for one
population than for other.
[0119] Whether one moves with a specific purpose or mindlessly
moves in "auto-pilot" mode, movement has inherent variability that
is objectively quantifiable. All things being equal, the
statistical signature of movement variability is unique to each
person and is revealing of mental states and of mental illnesses.
In this sense movement variability can become the bridge to connect
the mind and the body and to provide appropriate means to improve
social awareness.
[0120] The movement sense (kinesthesis) as other senses (vision,
audition, vestibular input, etc.) is a form of sensory input that
shapes the path of everything that one learns, yet movement can
also channel out through its inherent NSR- and variability-patterns
the most adequate form of sensory guidance to aid the system to
learn to heal itself. Such motor-sensorial preferences can be
extracted and exploited in a reward-reinforcement-based cooperative
person-robot setting to help stimulate creative and abstract
thinking through hands-on co-adaptive interactions, parts of which
can occur without full awareness and without explicit
instructions.
[0121] The same research program can also be carried out with
children in the spectrum of both genders from four to fifteen
(4-15) years of age. These children also became engaged in the
closed-loop video-triggering guided by the feedback from their hand
movement in real time, yet their progression towards intentionality
was slower than that of their TD peers and had a reversed
progression. TD exploration transitioned from random to systematic
to well-structured to intended-some TD children even verbalized the
contingency of arm movement and video appearance. In marked
contrast ASD started abnormally systematic (mechanic) and nearly
noiseless, transitioned through chaotic phases with no discernible
patterns and in some cases started to acquire similar exploratory
features of the TD. In the TD children these included detectable
systematic fluctuations in the distance traveled by the hand as the
hand crossed the virtual planes with corresponding changes in the
speed profiles of the hand which switched the statistical patterns
of variability of the hand peak velocity from exponential to skewed
lognormal and eventually to bimodal distributions signaling speeds
from two complementary space regions.
[0122] In both TD and ASD this progression strongly depended on the
child's favorite form of sensory input. In some cases the real time
video of themselves was quite effective in ASD whereas for other
children with ASD it was not as engaging. However, the children
with ASD can do the task without instructions, even
low-functioning, non-verbal. There is also a form of sensory
guidance that is quite effective in engaging them in exploratory
behavior towards intentional acts which can be objectively measured
in their spontaneous motions. Additionally the child with ASD with
echolalia and the verbal child with AS in the group were extremely
engaged and did show remarkable changes in a matter of seconds
across multiple sessions. The beneficial effects of this training
tool were strong, fast (a few minutes a day) and consistent across
all the TD children but they also showed promise in the children
with ASD.
[0123] Since the children can be engaged in active exploration
until they discover that they control an external stimulus and
pursue that control, the real time self-video can be replaced with
a robot (e.g., NAO robot) that can be modified and endowed with the
statistical range of movement variability from the typical
children. This robot will co-adapt its movements with those of the
child, initially recruited by the child. In time, once the robot
detects systematicity and that it is being controlled by the child,
it will be programmed to gradually shift the spontaneous components
of the motions into slightly different statistical patterns until
the child catches up with it and spontaneously reverts to try and
control the robot. The interface may be designed in closed loop as
a game to make it attractive to the child and to store the
adaptation trajectory for later to be used as reference when
comparing it to the trajectory of the children with ASD.
[0124] The motion detection algorithms may be developed to program
the robot to detect differences between random patterns, chaotic
patterns, systematic patterns, well-structured patterns and
intended patterns towards an emerging, well defined goal.
[0125] This interface may present snips from real social situations
and may be used to probe the TD children, for problem solving in a
virtual social setting and to enhance various aspects of ToM. Well
established paradigms may be used that probe the young children's
abilities for pretend-play, deception, implicit false belief,
understanding intention, and word-learning. The statistics of both
the intended and spontaneous motions that have been harvested and
parameterized in natural settings may be used to introduce as the
seed and then slowly reshaped in the virtual settings in order to
broaden the range of patterns present in the child's behavior as
the child co-adapts with the robot. In this way, awareness of
automatic body motions during problem solving in the TD children is
increased, which largely contribute to the highly automatic
inferences in ToM. Since metrics of performance gains have been
developed and tested across multiple populations of human and
non-human primates, gains in behavioral performance as the child
learns can be objectively quantified. This allows for the design of
a tool/device to precisely quantify the form of "automatic
intelligence" and identify it with automatic behavior. The instant
invention may also be used to enhance awareness in TD young
children of the cognitive difficulties in others to avoid bullying
situations and to foster understanding of others.
[0126] Understanding and objectively measuring movements link body
and brain. Building this link computationally will be fundamental
to foster proper development of the subject's mental abilities in
society. The instant approach to movement control and embodied
cognition facilitates the objective quantification of behaviors in
naturalistic settings--such as the classroom environment and the
home settings and permits the objectively quantitative tracking of
movement performance and cognitive-based motor learning gains over
time. Since the subject with ASD has social impairments, a
framework that spontaneously--without instructions--engages them
with virtual agents may first be developed. Subsequently, the
actual robots may be introduced to encourage the children's active
exploration and initial control over the robots, only to have the
robots gradually shift the statistical signatures of variability in
the subject with ASD towards typical ranges.
[0127] Since the statistical signatures are so far apart in TD and
ASD, any robot can be easily programmed to use the natural
statistics of movement to distinguish when it is interacting with a
TD child from when it is interacting with a child that has ASD.
Furthermore, since typical movements can be unambiguously
classified into spontaneous and intended based on the effects of
dynamics on their trajectories; but since this distinction is
blurred in ASD, it will be feasible for a robot to detect motions
from each child type and be programmed to respond in compliance.
These natural statistical features of physical movements may be
exploited to co-adapt robot and child as they interact using the
appropriate noise and variability levels and to automatically
(without the child's awareness) shift motor variability towards
levels that promote facial expressions, body gestures and body
language for non-verbal communication thus boosting social
interactions. This can be achieved in closed loop using real time
movements captured by motion sensors that are paired with other
sensory input of the child's preference. Three novel
characteristics of the paradigm include, without limitation: (1)
The automatic components of the wholesome movement unit, of which
the child has no awareness are targeted--rather than restricting to
the study of the intended, goal-directed component; (2) A goal is
not specified. Rather, the child discovers the goal of the task
through exploration; and (3) The statistics of facial expressions,
emotions and body movements (intended and automatic) of both TD
children and children with ASD have been parameterized (thereby
providing ground truth for training the robots).
[0128] The instant methods help blend TD children with peers who
have ASD. In the school system the methods will raise awareness and
understanding in the TD children of the motor/communication
problems in ASD and promote their willingness to approach their
peers socially and to interact with them and, crucially, to avoid
bullying in general. In turn by measuring and gradually shifting
the statistical signatures of movement variability in ASD towards
TD levels, the ASD children will be better able to blend in the
social scene as others will perceive them within the ranges of
socially acceptable motions. Importantly the children with ASD will
not have to directly imitate the motions of their TD peers through
explicit instructions. They will not be instructed to intentionally
do anything. Rather their automatic behaviors will be used and the
statistical patterns of their spontaneous movements will be
reshaped without their awareness to evoke the transition towards
intended behavior related to stimuli of their preference. These
children's interactions will contribute to the improvement of their
social and communicative skills in the classroom settings and
beyond while circumventing the known problems that children with
ASD have regarding imitation, verbal communication and cognitive
understanding in general.
[0129] A mobile child-machine interface system has been developed
that enables one to visit the classroom settings and have TD
children interact with touch screens and perform cognitive-driven
tasks adapted from their curricula. The statistical patterns of
natural movements--both voluntary and automatic--were first
collected across a variety of natural tasks including those of the
classroom settings involving the hands and upper body and others
engaging all limbs, the trunk and the head in sports routines such
as beginner's martial arts. All of the data was first parameterized
and may be used as a source to train the robots move naturally in
order to facilitate engagement with the children. Unconstrained,
natural movements are recorded by the system as the child learns to
perfect the task and becomes familiar with the computer environment
as a whole. The initial version of this interface was in open loop.
Children responded to stimuli presented on the touch screen and
pointed at the correct target that matched a given sample evoked by
their touch of the screen. The stimuli had perceptual and cognitive
features that varied in increasing levels of complexity from purely
visual (e.g., color) to more abstract (e.g., geometric shapes) and
even yet to more complex features that required mental rotation to
correctly match the given sample. The movements of the hand, arm,
trunk and head were concurrently recorded with the hand-screen
touches and video input from a camera facing the child, with all
behavioral events time stamped and logged for further off-line
analyses. Movements of the hand homing in on the target and
immediately preceding touching the screen were intended towards the
target or towards the sample to be matched. These intended
movements touched those locations on the screen. Spontaneously
retracting motions were automatic in that they were transitional,
did not pursue a goal, were not instructed and merely supported the
goal-directed component of the whole reach. These motions were
being carried passively along as the body spontaneously recovered
from the goal-directed portion. They engaged the full body and were
very revealing of the dynamics of the task. Their hand trajectories
changed dramatically with speed--unlike their intended counterpart
aimed towards the targets. The latter could be well characterized
as unique geodesics curves on a Riemannian manifold in that they
remained on the intended track, were speed- and loads-invariant
with low variability and locked in time with the trunk and head
immediately preceding the reach initiation as the decision to
choose a target was being made.
[0130] The instant invention may also be used to evoke automatic
cognitive abilities in the children with ASD. This may be done in
the context of ToM paradigms, borrowing specific scenarios from it.
The instant invention may be used to specifically identify abnormal
reflexes in the ASD children, known to be problematic in newborns
that go on to develop ASD and AS. The data bank of statistical
signatures may be parameterized in both intended and spontaneous
automatic movements in TD and used as a template to detect abnormal
patters and to correct them in ASD via the robot. The
identification of residual reflexes or their absence thereof will
guide the programming of movements that the robots will use to
co-adapt with the child. It has already been shown that the
individual statistical signatures of the natural movement
variability in the children with ASD can be shifted towards the
normal ranges and that they can perform these experiments in closed
loop using real time video-based and self-motions as forms of
sensory input.
[0131] Once the children with ASD are comfortable with the concept
of moving in tandem with an external agent and initiating the
robot's (avatar's) hand motions in closed loop with the children's
hand movements, the recruitment of other robot body parts may be
gradually initiated (e.g., NAO has twenty five (25) degrees of
freedom) by the child. It is known that systematic changes in
sensory input reshape the dynamics of their natural movements and
these changes can be precisely and objectively quantified across
time as the system learns new behaviors. In so doing, it has been
possible to decouple DoF that are devoted to intended behavior and
that are task relevant from DoF that are incidental to the task,
changing spontaneously and subject to different ranges of
variability with changes in dynamics. The evolution of the ASD body
can be closely monitored as it engages in tandem with the robot so
that eventually the child with ASD comes to spontaneously control
the ASD robot, without instructions. Fun movements to play may be
used that have already been tested in ASD including beginners'
martial arts routines and simple instrumental reaching and grasping
acts.
[0132] The lack of ToM is unique to children with high severity
scores of ASD, often without spoken language. Children with Down
syndrome or other mental disorders do not entirely lack ToM or
pretend-play abilities. Moreover some children with ASD can develop
deliberate, explicit ToM by twelve (12) years of age and solve the
problems that developmental psychology have created to probe their
cognitive abilities. They cannot however develop the type of ToM
that is implicit, fast, automatic and intuitive. The automatic
motions of the children with ASD may be monitored as they interact
with their TD peers and the two robots may be used as proxies to
promote social exchange. The TD robot information will provide the
trajectory in normal development whereas the ASD robot will provide
the error data.
[0133] The results from the instant methods will lead to new
discoveries about how the mind-brain interacts with the body and
spontaneously self discovers new solutions to problems. The
paradigm of tapping into automatic processes that can be
objectively quantified through the statistics of the variability
inherently present in natural repeats of unconstrained movements
will lead to the understanding of "automatic intelligence" and will
broaden the understanding of the spontaneous emergence of ToM
during typical development as well as through atypical development.
The outcome from the instant methods will provide a set of metrics
that enable one to systematically link motor variability and
normal/abnormal mental development. It will also enable one to link
motor variability with mental illnesses that affect cognitive
disabilities specific to improper social interactions. It will
create the first comprehensive parameterization of facial
expression and emotion statistics ranging from infants to young
adults with the corresponding set of body motions, including
reflexes, automatic and intended motions. Thus, it will provide the
first map identifying the statistics of facial motions and emotions
with the corresponding body dynamics across a large range of social
and non-social activities. This comprehensive tool will be of
utility to the robotics community modeling the phenomena as well as
to the clinical community trying to provide the appropriate
behavioral therapies.
[0134] In accordance with the instant invention, methods for
classification leading to diagnosing and/or providing a prognosis
for a neurological disorder, particularly an autism spectral
disorder, in a subject (e.g., animal, particularly human) are
provided. In a particular embodiment, the method comprises
measuring the motion pattern of the subject upon interaction with
an artificial agent, wherein the motion of the subject is observed
over a millisecond range (e.g., s-Peaks are observed/monitored). In
a particular embodiment, the motion is detected through the use of
wearable sensor. In a particular embodiment, the synchronicity
and/or periodicity of the fluctuations (changes) in the millisecond
range are observed. As used herein, the phrase "millisecond range"
may refer to a time frame that is less than one second,
particularly less than about a half (0.5) second, particularly less
than about on hundred (100) milliseconds, less than about fifty
(50) milliseconds, less than about twenty five (25) milliseconds,
or less than about five (5) or ten (10) milliseconds. In a
particular embodiment, the motion of the subject (e.g., the speed)
is observed over segments of time in the millisecond range (e.g.,
from about one to about three (3) millisecond, from about one (1)
to about five (5) milliseconds, from about one (1) to about ten
(10) milliseconds, from about one (1) to about twenty five (25)
milliseconds, about one (1) to about fifty (50) milliseconds, or
about one (1) to about one hundred (100) milliseconds). In a
particular embodiment, the subject is not provided instruction on
how to interact with the artificial agent. The artificial agent
provides a stimulus when the subject contacts a region of interest
(e.g., a virtual region of interest such a three dimensional (3D)
space). A difference in the motion pattern of the subject compared
to a healthy individual and/or the presence of a motion pattern
associated with an autism spectral disorder indicates that the
tested subject has an autism spectral disorder. In certain
embodiments, the artificial agent is a dynamic media or interface
and may be a robot, three dimensional animate, speaker, or
multi-touch surface screen. In a particular embodiment, the
artificial agent is a screen (e.g., comprising a target) and the
stimulus is a real-time video of the subject. The methods of the
instant invention may further comprise measuring other aspects of
the subject (e.g., facial patterns) upon interaction with the
artificial agent.
[0135] In accordance with another aspect of the instant invention,
the methods described herein can be used for determining the
sensory capabilities and preferences of an individual. Once
determined this sensory modality is used in therapies that examine
the patterns of random fluctuations--particularly over a
millisecond range--of movement parameters in the movement
trajectories of the person's body parts (e.g. hands, head, trunk,
limbs, etc.). These random fluctuations over time (over repetitions
of the same behavior) serve as a form of re-afferent sensory input
that the system integrates with the efferent motor output and
utilizes these inputs differently as a function of cognitive
complexity in closed loop with cognition. In a particular
embodiment, the methods use a set of postures to trigger external
media (audio, videos, real time self-videos from a webcam facing
the child, or virtual variants of the child embodied in a three
dimensional animate that is endowed with the child's physical
motions or with noisy variants of it). In a particular embodiment
the set of postures thus learned by the subject are associated with
intuitive gestures for communication that operate and control
external media (e.g. play, rewind, pause, fast-forward, flicker,
etc.). In a particular embodiment, the method further comprises
measuring a motion pattern of the subject--particularly over a
millisecond range--prior to the administration of a therapy (which
could be either pharmaceutical or behavioral or both; e.g., to
obtain a baseline measurement). In a particular embodiment, the
method comprises measuring the motion pattern of the
subject--particularly over a millisecond range--upon interaction
with an artificial agent as described above, after administering
the therapy to the subject to measure performance gains relative to
baseline values. The modulation of the motion pattern of the
subject after administration of the therapy (e.g., to a normal
motion pattern) indicates that the therapy modulates the autism
spectral disorder. The direction of this modulation (away or
towards typicality, or neutral meaning no change) is evaluated so
the effectiveness of treatment can be objectively determined.
[0136] In accordance with another aspect of the instant invention,
methods of reshaping the spontaneous random noise into
well-controlled motion patterns of a subject with an autism
spectral disorder are provided. In certain embodiments the subject
is allowed to control an artificial agent which initially moves
with the stochastic signatures from the dynamics extracted from the
physical motions of the subject--particularly over the millisecond
range. Gradually the stochastic signatures of the artificial agent
(three dimensional animate or robot) are reshaped so as to
harmoniously co-adapt the subject and the artificial agent. In
certain embodiments, the method comprises having the subject
interact with an artificial agent as described above. In a
particular embodiment, the artificial agent is a robot,
particularly one programmed to encourage the subject to
spontaneously (without explicit instructions or goals) react and
move similarly to typically developing children. The subject is in
control. The changes work because they are applied to movements
that are spontaneous and occur without the subject's intent. These
are the movements that do not conserve their motion trajectories as
the dynamics of the motion change. The movements that conserve
their trajectories and remain invariant to changes in dynamics are
the ones under voluntary control and will resist spontaneous
changes. Therefore the technique exploits the motions that are
collateral, supplemental, and "invisible" to the conscious
mind.
[0137] As stated herein, the methods described throughout the
instant invention can be used for diagnosing, characterizing,
classifying (e.g., along a continuum spectrum), assessing, and/or
treating a neurological disorder in a subject. In a particular
embodiment, the subject is at least three (3) or four (4) years
old. Neurological disorders include neurodevelopmental and
neurodegenerative disorders. Specific examples of neurological
disorders include, without limitation: Parkinson's disease,
parkinsonian syndrome, Autism, Autism spectrum disorder,
Huntington's disease, athetosis, dystonia, cerebellar and spinal
atrophy, multiple system atrophy, striatonigral degeneration,
olivopontocerebellar atrophy, Shy-Drager syndrome, corticobasal
degeneration, progressive supranuclear palsy, basal ganglia
calcification, parkinsonism-dementia syndrome, diffuse Lewy body
disease, Alzheimer's disease, Pick's disease, Wilson's disease,
multiple sclerosis, peripheral nerve disease, brain tumor, cerebral
stroke, attention deficit hyperactivity disorder (ADHD), Down
syndrome, William syndrome, schizophrenias, etc. In a particular
embodiment, the neurological disorder is Autism, Autism spectrum
disorder, or Parkinson's disease.
[0138] In a particular embodiment, the method of the instant
invention comprises measuring the motion pattern of a
subject--particularly within the millisecond range--upon
interaction with an artificial agent, wherein a difference in the
motion pattern of the subject compared to at least one control
(e.g., a healthy individual and/or an individual with a
neurological disorder indicates whether the subject has the
neurological disorder and/or the classification or severity of the
neurological disorder). In a particular embodiment, the gender
and/or age of the subject and the control standards are the same.
In a particular embodiment, the artificial agent provides a
stimulus when the subject contacts a region of interest. The
artificial agent may provide a target or cue to the subject (e.g.,
a target to touch, such as a dot or light). In a particular
embodiment, the artificial agent provides a challenge or test
(e.g., match-to-sample test) to the subject that requires the
movement and selection of an answer by the subject. For example and
as described herein, the artificial agent may present an object and
require the subject to touch the object by distinguishing between
the object and a second object which differs from the first object
by color, shape, orientation, or the like. The subject may or may
not be provided instruction on how to interact with the artificial
agent. The artificial agent may be a dynamic media or interface. In
a particular embodiment, the artificial agent provides a video
(e.g., a video of movements by an individual (e.g., karate moves)
which can be mimicked by the subject). Examples of an artificial
agent include, without limitation a robot, three dimensional
animate, speaker, screen (e.g., computer screen), touch screen,
tablet (e.g., iPad), and the like. In a particular embodiment, the
artificial agent is a screen (e.g., a touch screen).
[0139] In a particular embodiment, the motion pattern of the
subject's arm is measured particularly within the millisecond
range. However, any body part can be measured (e.g. hands, head,
trunk, limbs, etc.). In a particular embodiment, the difference in
size of the body parts (e.g., limb size) of subjects and controls
is accounted for (e.g., normalized). The intentional or deliberate
motions (e.g., those aimed at a target) of the subject may be
measured and compared to standards and/or the automatic or
spontaneous motions (e.g., the retracting from a target) may be
measured and compared to a standard. Any parameter of the motion of
the subject may be measured. Parameters that can be measured
include, without limitation: speed profile, max speed, time to
reach maximum speed, acceleration, max retraction speed, time to
reach max retraction speed, three-dimensional path, accuracy of
target touching, percentage correct (when the artificial agent
provides a test), overall amount of time for motion, decision
movement latency, body part rotation or positioning, and joint
angle. In a particular embodiment, changes within the speed of the
motion in the millisecond range are measured. The method of the
instant invention may also comprise measuring and/or monitoring the
facial patterns of the subject during interaction with the
artificial agent. In a particular embodiment, the subject is placed
in a particular position or orientation (e.g., a primed position)
prior to interacting with the artificial agent.
[0140] When the artificial agent provides a stimulus (e.g., turns
on a stimulus) when the subject contacts a region of interest, the
stimulus may be a real-time video of the subject. In a particular
embodiment, the stimulus is a video, such as a cartoon video. In
yet another embodiment, the stimulus is a three dimensional
animate.
[0141] As stated hereinabove, the instant invention also
encompasses methods for determining the ability of a therapy to
modulate a neurological disorder in a subject. In a particular
embodiment, the method comprises administering the therapy to a
subject and performing at least one of the above diagnostic methods
of the instant invention (e.g., monitoring motion upon interaction
with the artificial agent, particularly within the millisecond
range) to determine whether the administered therapy modulated
(e.g., treated) the neurological disorder (e.g., by comparing to
standards or previously obtained standards of the subject). In a
particular embodiment, the method comprises performing at least one
of the diagnostic methods of the instant invention, administering
the therapy to the subject, and performing a second diagnostic
method of the instant invention on the subject, wherein a change in
the second assay compared to the first assay indicates that the
therapy modulates the neurological disorder. For example, if the
results of the second assay more closely approximate the pattern of
a healthy individual than the first assay, the therapy is effective
against the neurological disorder.
[0142] In accordance with another aspect of the instant invention,
methods of treating a neurological disorder and/or lessening the
improper motion pattern of a subject with a neurological disorder
such as an autism spectral disorder are provided. In a particular
embodiment, the method comprises having the subject interact with
an artificial agent which provides a stimulus when the subject
contacts a region of interest, wherein the interaction of the
subject with the artificial agent lessens the improper motion
pattern associated with the neurological disorder particularly
within the millisecond range.
[0143] The following example is provided to illustrate certain
aspects of the present solution. The present solution is not
intended to limit the invention in any way.
Example
[0144] A critical need in autism research and its treatments is to
find an efficient quantitative way to categorize the disorder. In a
given cohort of affected people there is high probability that not
two individuals are alike, even when their ADOS (Lord et al. (2000)
J. Autism Dev. Disord., 30:205-223) scores may classify them
similarly. One may find, for example, that two children who are
classified as medium functioning show surprising differences in
their individual capabilities and predispositions that set them
apart.
[0145] The active field of computational neuroscience and in
particular the subfields of sensory-motor physiology and motor
control may provide this bridge because behaviors that are verbally
described by clinicians can be objectively quantifiable. Behaviors
are composed of many movements with different levels of intent
(Tones, E. B. (2012) Neurocase, 1:1-16; Tones, E. B. (2011) Exp
Brain Res., 215:269-283). They flow as a continuous stream of
motions that occur at different bodily levels and have underlying
quantifiable sensory-motor physiology. There is thus a critical
need for finding objective biometrics providing neurophysiological
characterizations of different behaviors that can be done by active
research groups working on autism sensory-motor neuroscience
research.
[0146] Recent studies from various groups have uncovered deficits
in the two-way exchange of motor command and sensory information in
motor control as potentially fundamental core symptoms of ASD,
across ages and in both sexes (Deitz et al. (2007) Phys.
Occupation. Ther. Ped., 27:87-102; Whyatt et al. (2012) J. Autism
Develop. Disorders 42:1799-1809; Torres et al. (2013) Front.
Integr. Neurosci., 7:32; Torres et al. (2013) J. Neurophysiol.,
110:1646-1662; Haswell et al. (2009) Nat. Neurosci., 12:970-972;
Marko et al. (2015) Brain 138:784-797). However, this nascent field
of sensory-motor research in autism has yet to penetrate mainstream
clinical practices. Moreover, possible connections between
sensory-motor control and cognitive abilities such as spoken verbal
abilities have not yet been considered.
[0147] The human eye observing and qualitatively evaluating
behaviors has limitations and necessarily misses subtle millisecond
movements that occur largely beneath awareness. With the advent of
new high resolution wearable sensors (Allet et al. (2010) Sensors
10:9026-9052; Burns et al. (2010) Conf Proc IEEE Eng Med Biol Soc.,
2010:3759-3762) it is possible to obtain valuable information that
otherwise escapes the observer's eyes. Using such instrumentation,
important statistical signatures were found hidden in the
micro-structure of motor output variability inherently present in
peripheral limb movements (Jones et al. (2013) Nature 504:427431;
Gepner et al. (2001) J. Autism Dev. Disord., 31:37-45; Torres et
al. (2013) Front. Integr. Neurosci., 7:32). These physiological
motion signals from the Peripheral Nervous Systems (PNS)
unambiguously separate the course of maturation of autistic from
typically developing individuals (Tones, E. B. (2013) Neurocase
19:150-165; Tones et al. (2013) Front. Integr. Neurosci., 7:32;
Tones et al. (2013) Front. Integr. Neurosci., 7:46; Tones et al.
(2013) J. Neurophysiol., 110:1646-1662). After four (4) years of
age, peripheral kinematics undergoes a maturational change that
does not occur in ASD, regardless of age, sex or ADOS scores
(Tones, E. B. (2013) Neurocase 19:150-165; Tones et al. (2013)
Front. Integr. Neurosci., 7:32; Tones et al. (2013) Front. Integr.
Neurosci., 7:46; Tones et al. (2013) J. Neurophysiol.,
110:1646-1662).
[0148] These findings enable reliable detection of autistic traits
using critical points of the velocity trajectories, e.g., their
absolute global maxima (Tones, E. B. (2013) Neurocase 19:150165;
Tones et al. (2013) Front. Integr. Neurosci., 7:32; Torres et al.
(2013) Front. Integr. Neurosci., 7:46; Torres et al. (2013) J.
Neurophysiol., 110:1646-1662). Here, local, much smaller maxima
were also found to be present in these hand trajectories. These
local smaller peak fluctuations in the speed were found to provide
finer grain detailed information about ASD.
[0149] Traditionally, when analyzing raw kinematics data, the
fluctuations along the position time series are considered noise
and often averaged out. In particular, in the past, local peak
fluctuations in the kinematics data have not been of interest to
movement neuroscientists. To assess the possible relevance of those
fluctuations it is important to use smoothing techniques which
preserve elements of the original temporal structure of the raw
data upon smoothing that may contain relevant information. To
ensure that the possibly important original peak fluctuations were
not averaged out here we chose to use a triangular smoothing
procedure (Simonoff, J. S. Smoothing methods in statistics
(Springer, 1996)) that preserves the original peak fluctuations in
the temporal structure of the raw data within the millisecond time
range. The statistical structure and properties of the resulting
time series of those movement fluctuations were examined (FIGS.
13-14 and 18-19).
[0150] The velocity-dependent fluctuations (both global and local)
were termed Peripheral Spikes (s-Peaks-Spikes) because they are
physically recorded from the peripheral limbs using high-resolution
sensors that the person physically wears. Such wearable sensors
"listen" to the physiological signal and noise blend that the
muscles naturally amplify from motor and sensory nerves under the
skin. This is in contrast to the kinematics signal estimated from
observed in video sequences, which require numerous coding
heuristic interpolations to recover missing frames from occlusions,
or to extract discrete segments that the observer determines are of
relevance. The sensors sampled at two hundred forty (240) Hz
continuously capturing the actual physical movements. The
analytical approach used recently provided an objective
longitudinal profiling of peripheral limb movements' development
across different ages in a heterogeneous cohort (Torres et al.
(2013) Front. Integr. Neurosci., 7:32). Here it was aimed at
blindly characterizing individuals in the heterogeneous spectrum of
autism in relation to their progenitors. New statistical links not
previously considered were uncovered since they emerged from direct
measurements without human subjective human interventions.
[0151] A simple forward-and-back pointing paradigm was used to
continuously record the s-Peaks (FIG. 13A). A modified version of
the traditional pointing paradigm was used (Torres et al. (2013)
Front. Integr. Neurosci., 7:32). In the continuous flow of hand
motions, the deliberate reach-out segments directed towards the
instructed target was distinguished from the hand-retraction
segments that spontaneously took place without any instruction or
visual goals. FIG. 13B-13D show sample trajectories from three
representative subjects. Although at first glance the positional
trajectories look very smooth, when one zoomed in the temporal
dynamics covering these hand positional paths, one found
millisecond range speed fluctuations that clearly differed across
subjects from low-functioning non-verbal, high-functioning verbal
to typical control (FIG. 13E-13G). A typically developing
individual completed several cycles of forward-and-back motions,
within any given time window, along continuous flow of hand
movements, (e.g. 8 s in FIG. 13G). In stark contrast individuals
across the spectrum of autism had systematically increasing number
of s-Peaks both between and within the forward-and-back cycles. The
lower the verbal/communicative skills were, the higher were the
numbers of s-Peaks present in their motions. Consequently, there
were fewer full cycles per unit time. Zooming in further in one
full cycle of motion for a low-functioning non-verbal child and a
typically developing child revealed a dramatic difference in the
smoothness of the motion cycle itself (FIGS. 13A-13I).
[0152] Trial-by-trial s-Peaks were examined while searching for
patterns of synchronous behavior across cycles. To this end, the
touches were aligned at zero (0) ms time and a large enough time
window that included both the forward and the retraction movements
was taken. FIG. 14A shows a s-Peaks vector spanning one thousand
(1,000) ms before the touch at the start to one thousand (1,000) ms
after the touch. By stacking up these s-Peaks vectors along the
continuous motion flow, a s-Peaks matrix was formed. In analogy to
what is done with action potential CNS spikes we plotted the
s-Peaks vectors in rastergram-like form. FIG. 14B-14D show s-Peaks
matrices from different representative subjects in the cohort,
including the representative typical control cases. The global
speed maxima are aligned across trials relative to the touch. In
the control subject (FIG. 14D) there are no s-Peaks in the
acceleration or in the deceleration phases of the forward or
backwards movements of each cycle. The other participants, who had
a diagnosis of ASD, had very different features. The systematic
increase in the lack of structure of the s-Peaks matrix coincided
with the reported verbal spoken abilities of the cohort. The
visualization tool revealed an orderly trend: the fewer the verbal
spoken abilities, the more disordered the structure in the s-Peaks
matrix.
[0153] This systematic relationship prompted further examination of
various stochastic features of these continuous random processes in
more detail. Trial-by-trial (population) cross-correlation and
patterns of synchronization (or lack thereof) were assessed for
each individual member of this cohort of sixty-five (65)
individuals. Included in the cohort of sixty-five (65) subjects
were: thirty (30) subjects diagnosed with ASD with ages from seven
(7) to thirty (30) years old; eight (8) adult controls, six (6)
typical developing (TD); three (3) to five (5) years old children,
and twenty one (21) ASD's parents. All the ASD individuals were
diagnosed in the spectrum by professionals/agencies qualified to do
the testing. The demographic information for all participants
studied is listed in TABLES 1-2. The labels from the clinical
reports were used as references: low functioning (LF: no spoken
language); mid functioning (MF: some spoken words) and high
functioning (HF: some communicative phrases).
TABLE-US-00002 TABLE 1 ADOS Scores GARS Scores M/ Age
Stanford-Binet Com + Stereo Com Soc Autism Parent Code F Yrs NVIQ
VIQ FSIQ Stereo Com Soc Soc SS SS SS Index Mo, Fa 1 M 10 N/A N/A
107 3 3 9 12 N/A N/A N/A N/A 2 M 10.3 42 43 40 3 4 10 14 N/A N/A
N/A N/A 3 M 11.5 100 82 90 7 5 6 11 N/A N/A N/A N/A 4 F 11.5 50 43
4 N/A N/A N/A N/A N/A N/A N/A N/A 5 M 11.7 42 43 40 5 8 10 18 N/A
N/A N/A N/A 6 M 11.7 43 43 40 N/A N/A N/A N/A N/A N/A N/A N/A Mo,
Fa 7 M 12 N/A N/A 67 4 5 13 18 N/A N/A N/A N/A 8 F 12 N/A N/A 60 4
8 10 18 N/A N/A N/A N/A Mo, Fa 9 M 12 N/A N/A 95 2 5 8 13 N/A N/A
N/A N/A Mo, F 10 M 12 N/A N/A 95 1 5 7 12 N/A N/A N/A N/A Mo 11 M
13 N/A N/A 89 2 3 7 10 N/A N/A N/A N/A 12 M 13.8 42 43 40 N/A N/A
N/A N/A N/A N/A N/A N/A Mo, Fa 13 M 14 N/A N/A 74 3 9 18 19 N/A N/A
N/A N/A 14 F 14.3 50 43 44 N/A N/A N/A N/A 8 11 9 124 Mo 15 F 15
N/A N/A 52 2 6 11 17 N/A N/A N/A N/A 16 F 15 N/A N/A 77 N/A N/A N/A
N/A N/A N/A N/A N/A Mo 17 F 15 N/A N/A 71 N/A N/A N/A N/A N/A N/A
N/A N/A 18 M 15 N/A N/A 56 3 4 10 14 N/A N/A N/A N/A 19 F 15 N/A
N/A 52 2 6 11 17 N/A N/A N/A N/A 20 F 15 N/A N/A 7 N/A N/A N/A N/A
N/A N/A N/A N/A 21 M 15 N/A N/A 71 6 5 7 12 N/A N/A N/A N/A 22 F
15.8 42 43 40 N/A N/A N/A N/A 13 10 11 109 Mo, Fa 23 M 14 N/A N/A
100 100 N/A N/A N/A N/A N/A N/A N/A Mo 24 F 16 N/A N/A 81 2 7 9 16
N/A N/A N/A N/A Mo 25 M 18 N/A N/A 101 2 4 6 10 N/A N/A N/A N/A Mo
26 M 18 N/A N/A 96 4 4 8 12 N/A N/A N/A N/A Mo, Fa 27 M 18 N/A N/A
76 1 5 7 12 N/A N/A N/A N/A 28 F 19 N/A N/A 55 N/A N/A N/A N/A N/A
N/A N/A N/A Mo 29 M 25 N/A N/A 99 6 3 7 10 N/A N/A N/A N/A Mo, Fa
30 M 30 N/A N/A 36 2 8 14 22 N/A N/A N/A N/A
(Autism Diagnostic Observational Scale) (Lord et al. (2000) J.
Autism Dev. Disord., 30: 205-223; Gotham et al. (2009) J. Autism
Dev. Disord., 39:693-705) is a standard assessment tool used by
clinicians as a basis for the ASD diagnosis. Module 1 of the ADOS
was used for the young, non-verbal students. Module 3 was used for
the adolescent students with conversational ability. Stereo is a
measure of stereotyped behaviors were a higher score indicates more
stereotyped behaviors; however without a cutoff for ASD
diagnosis.com is the total Communication score, where four (4) is
the cutoff for Autism and two (2) the cutoff for Autism Spectrum.
Soc is the total Reciprocal Social Interaction Score, where four
(4) is the cutoff for Autism, and two (2) the cutoff for Autism
Spectrum. Corn+Soc is the combined Communication and Social
Interaction score, with a score of twelve (12) being the Autism
cutoff, and seven (7) the Autism spectrum cutoff. Because of their
age and extremely limited verbal abilities, two (2) of the children
could not be given the ADOS. Therefore the GARS 2 (Gilliam Autism
Rating Scale--Second edition; Gilliam, J. (2006) GARS-2: Gilliam
Autism Rating Scale-Second Edition. Austin, Tex.: PRO-ED) was used
to assess these individuals. Stereo SS is the standardized score of
stereotyped behaviors. Corn SS is the standardized score of
Communication. Social SS is the standardized score of Social
Interactions. The Autism Index is the sum of standard scores,
converted to a normed index score. For the participants shaded in
gray we also recorded the parents under identical circumstances. Mo
is for mother and Fa is for father. Dark gray are siblings with the
same Mo and Fa (two males and one female marked with an
asterisk).
TABLE-US-00003 TABLE 2 Participant Gender Age 1 M 3 2 M 4.3 3 F 4.3
4 F 4.8 5 M 4.8 6 M 5.1 7 F 21 8 F 22 9 F 24
Table 2: Information from TD participants. Typically developing
children (1-6) and typical controls (shaded in gray).
[0154] Results
[0155] An orderly consistent increase in the number of s-Peaks and
their randomness emerged from the analysis that corresponded well
with the low-, mid-, high-functioning ASD diagnostic classification
(FIGS. 13E-13F). These were much less or entirely absent in the TD
subjects. Zooming in the movement temporal dynamics within one full
motion segment cycle, dramatic differences in the number of s-Peaks
appearing were identified (FIGS. 13A-13I). FIGS. 14B-14D further
showing this trend in the raster grams-like plots constructed from
the s-Peaks.
[0156] These panels show the increasing lack of structure in the
spike trains from controls (well structured) to high functioning
(some structure) to low functioning (random). The reports on the
precise quantification and characterization of the degree of
randomness and spoken abilities are provided.
[0157] A measure to assess the s-Peaks' repetitiveness or
synchronicity across cycles has been provided by calculating the
population cross-correlation C(.tau.) of the binned s-Peaks cycle
vectors as a function of binning size ".tau.". As illustrated in
the methods, the degree of synchronicity was characterized with the
second derivative of the C(.tau.) curves, i.e. the second
derivative of the C(.tau.) curve decreases as the synchronicity
increases. The empirical C(.tau.)curve was also compared to the
`total-random` curve C.sub.r(.tau.) calculated using a simulated
Poisson random s-Peaks process having the same occurrence rate and
cycle length as in the empirical data. The empirical case was
verified against the results from the numerical simulations and
analytical approach to also determine empirical boundaries for the
extreme cases (FIG. 20 simulation and FIG. 15 empirical
results).
[0158] FIGS. 15A-15C show the C(.tau.) curves with their quadratic
fits, compared to their corresponding `total random` curves
C.sub.r(.tau.) (dashed lines analytically derived as limiting
cases), for three representative subjects with different spoken
verbal abilities. The representative control subject's curve has a
decreasing slope as r grows, deviating away from its corresponding
total random curve, indicating partial s-Peaks synchronicity. The
curves for the representative subjects with HF- and LF-ASD show a
flat and increasing slope, respectively, indicating that the
decrease in s-Peaks' synchronicity correlates well with a
systematic reduction in spoken language ability. Notice that the
curve for the LF-ASD subject almost overlaps with its totally
random curve, indicating high randomness (i.e. the lack of any
synchronicity) of s-Peaks occurrences in this case.
[0159] FIG. 15D verifies the link between s-Spike's synchronicity
and spoken verbal abilities in the ensemble. Averaged C(.tau.)
curves for subgroups classified by spoken verbal ability (8 LF, 8
MF, 14 HF and 8 controls), are plotted with error bars early and
late in binned time. Note that the averaged curves are well
separated and automatically ordered matching the spoken verbal
ability classification. FIG. 15E further illustrates this
correspondence in the two dimensional ("2D") parameter plane:
second derivatives of the C(.tau.) curves and by the maximum
deviation in-between the C(.tau.) and the C.sub.r(.tau.) curves.
Average subgroups positions with different spoken verbal abilities
are plotted in the inset, showing the correlation between s-Peaks
synchronicity and spoken verbal abilities. The curves for ASD
subjects have positive second derivatives compared to the negative
values for control subjects and the curves for ASD subjects have
less deviation from the random curve compared to the control
subjects.
[0160] FIG. 21 shows results of the autocorrelation and Fourier
spectrum analyses for the three representative subjects supporting
the increase of s-Peaks synchronicity correlating to the increasing
of spoken verbal abilities. The movements studied here are natural,
not forced to be synchronized. For example, the movement's
durations might vary from trial to trial. This might contribute to
the lack of synchronicity of the s-Peaks. To address this issue,
further s-Peaks Interval Separation analyses was
performed as follows below.
[0161] As discussed in the Methods section, micro-dynamics
fluctuations were also studied. Specifically, the temporal
properties of the s-Peaks in the speed profiles was characterized
using the inter s-Peaks intervals (s-IPIs) in analogy with the
neuronal action-potential inter-spike intervals (IPIs). The
analyses commonly done for neuronal spikes in the central nervous
system were adapted here in the new data type obtained from the raw
kinematics read out from the peripheral limbs.
[0162] Frequency histograms were built with the s-IPIs in the full
forward-and-backward cycles (kinetic s-IPIs). As shown in FIGS.
17A-17C, across all subjects, small s-IPIs values are exponentially
distributed. The exponential contribution of the s-IPIs show the
total s-Peaks randomness. The non-exponential components of the
s-IPI's distributions were separated to illustrate their "away from
full randomness" contributions (lower panels in FIGS. 17A-17C).
These residual s-IPI histogram contributions capture the systematic
differences found across subjects with different spoken verbal
abilities. The histogram for the representative subject with LF-ASD
rarely had any residual s-IPIs components (FIG. 17A) having almost
all s-IPI values falling in the exponential region. This result
provides further support to the s-Peaks randomness in LF-ASD
obtained from the synchronization analysis described above.
[0163] In contrast to the LF-ASD, the s-IPI histograms for the
representative subjects with HF-ASD and the TD adult had
significant residual s-IPIs contributions away from the exponential
region (FIGS. 17B-17C). The residual histogram hump found in the
s-IPIs for the HF-ASD subject falls at the exponentially dominated
tail, while the hump for the control subject falls farther away
from the exponential range, having larger s-IPI values. The hump
with larger interval values mostly corresponds to the smoother
speed behaviors during ballistic movements with rare kinetic
s-Peaks (FIG. 13I and FIG. 14D).
[0164] As discussed in the Methods, to better visualize
quantitatively the differences among subjects from the histograms,
a parameter space defined by two statistical parameters was
constructed. The first parameter is simply the mean value of the
kinetic s-IPIs. To quantify the appearance of humps away from the
exponential region in the histograms we introduced a second
parameter R weighted by the separation values. This two-dimensional
plane with axes R (vertical) and mean s-IPI value (horizontal)
provides a concise map representation for each individual localized
by a specific point in the plane as shown in FIG. 17D. The results
from the cohort studied (30 with ASD and 8 adult controls)
automatically showed three (3) visually distinctive clusters in the
phase diagram. The K-means cluster algorithm was used for coloring
the points in varying shades of gray (cluster 1 has the lowest mean
s-IPI and lowest R). FIGS. 17E-17F show the decrease in population
cross-correlation C(.tau.), with the curve's second derivative and
the increase in its distance from randomness as the cluster index
increases. This parameter plane provides a quantitative ensemble
description further supporting the systematic traits found in the
s-IPIs histograms and the s-Peaks' synchronicity discussed in
previous sections.
[0165] As a test of this approach, the correspondence of the
automatic subject clustering was compared and verified and it was
found independently in the s-IPI parameter plane analysis described
above with the level of ASD severity as clinically determined by
the spoken verbal ability. In FIG. 18A, the subjects are coded
based on the degree of spoken verbal abilities (from lacking to
being able) and shape-coded based on the cluster index. The number
of subjects from each clinical subgroup fell a posteriori into each
gray-shaded cluster on the parameter plane illustrated in FIG. 18B:
all LF-ASD subjects (8/8) fell into cluster 1; most HF-ASD subjects
(12/14) fell into cluster 2; MF-ASD subjects fell either in cluster
1 (3/8) or cluster 2 (5/8); all control subjects (8/8) fell into
cluster 3. The HF-ASD subject falling into cluster 1 with zero
R-value is an outlier in our cohort.
[0166] The overall results show the agreement of the phase plane
location with the subject's spoken verbal abilities. The average
subject's location in each clinical subgroup (excluding the outlier
high functioning subject) is shown in the inset in FIG. 18A. These
results clearly provide strong evidence for a systematic increase
from the bottom left to the top right corners in the parameter
plane corresponding with the degree of spoken abilities. The
millisecond time range analysis of the s-Peaks separations
presented here enables the screening of subjects with ASD,
unambiguously distinguishing them from adult controls, and further
providing clear quantitative information about the severity level
of the ASD. This classification feature is explored next in
relation to age and maturational stages.
[0167] Most studies of ASD focus on children, as it is difficult to
recruit older subjects. Here a wider range of ages was included:
from seven (7) to thirty (30) years old. Under this broader range
of ages it was then asked if the s-Peaks signatures had any
systematic maturational trend that changed with ageing. Age is an
important factor in predicting cognitive milestones in TD controls.
Yet in ASD, the developmental process is atypical and may follow
different individual cognitive trajectories. To address the
question on possible maturational stages, six (6) TD young
individuals (3-5 years old) were included in the cohort, in
addition to the eight (8) TD adults previously discussed. The
statistical signatures based on the single speed maxima in the
pointing motion task identified had unveiled an important
transitional maturation threshold in the statistical signatures
taking place after five (5) years of age (Tones et al. (2013)
Front. Integr. Neurosci., 7:32). In that study the three (3) to
four (4) year olds and the adult groups served as limiting typical
subgroups to set anchors on the Gamma parameter space. The
individuals with ASD in the present study have a broader age
spectrum (from 7 to 30). This allowed one to more clearly explore
whether or not the group with ASD matures towards the same patterns
found in typical adults or not. Those patterns would also describe
about locations of affected subjects in the s-IPI parameter plane
relative to TD young subjects before reaching full maturation.
[0168] Note that the parameter plane in FIG. 18C clearly shows a
separation between TD young children and typical adults, providing
the maturation evolution trajectory of TD subjects from bottom left
to top right in the plane. On the other hand, subjects with ASD,
across the broader range of ages, remained in the same lower left
region of the plane, clustered together with the three (3) to five
(5) year old TD children. They did not show any evidence of
transitional maturation registered in typical development. This is
clearly shown in the inset of FIG. 18C with average positions for
younger ASD subjects up to fifteen (15) years of age clustering
together. This result indicates that, within the biometrics defined
here and in terms of movements the ASD subject's age might be
developmentally irrelevant: a 30 year old ASD subject may fall in
the same region as a ten (10) year old or a fifteen (15) year old
subject. This indicates that the nervous system with ASD is
continuously coping with the disorder and evolving along atypical
developmental trajectories. The result further emphasizes that
grouping individuals with ASD by age, as it is traditionally done,
may blur relevant information about the levels of maturity of their
coping systems and their unpredictable longitudinal evolution.
[0169] Behavioral phenotyping of ASD is currently done by
observation and subjective verbal reports. These methods preclude
correlating genetic information with behavioral phenotypes. The new
biometrics introduced here allows one to examine, whenever
possible, the patterns of parents' motions and those of their
affected children. The literature suggests high heritability of ASD
based on reports of significant concordance of ASD in twins (Ronald
et al. (2011) Amer. J. Med. Genet., Part B, 156:255-274) and the
high recurrence risk (Ozonoff et al. (2011) Pediatrics
128:e488-e495) in families. ASD risk has been reported to increase
with increasing relatedness (Sandin et al. (2014) JAMA
311:1770-1777). The inventories in use to phenotype ASD have high
clinical and genetic heterogeneity even in affected siblings (Yuen
et al. (2015) Nat. Med., 21:185-191) posing a significant challenge
to bridge the gap between the current phenotyping and the
underlying genetics. Bridging this gap would be critical to the
development of target treatments in ASD.
[0170] Here, the question of whether the s-Peaks' patterns found in
their children are similar to those present in their parents'
movements was addressed. Previous studies reported an increasing
risk of having communication and social difficulties (known as
"broad phenotype") in non-diagnosed ASD relatives (Bishop et al.
(2006) Amer. J. Med. Genet., Part B, 141:117-122). The quantitative
metrics introduced here for movement assessments enables one to
address a possible relationship with millisecond time
precision.
[0171] To address this question, twenty one (21) available parents
of fourteen (14) ASD participants were included. The corresponding
results are plotted in FIG. 18D. The parents' signatures were
mostly localized together with the ASD subjects, away from the
typical adult controls in the background (same nomenclature as in
FIG. 17D). The figure shows thirteen (13) out of the twenty one
(21) parents (11 out of 14 mothers, 2 out of 7 fathers) localized
in the same ASD region clustered as well with their affected child,
clearly away from typical adults. This result can also be seen in
FIG. 18E where an oriented Euclidean norm was used to plot the
oriented distance for each parent and typical young controls from
the TD three (3) to five (5) year old cluster centroid (negative
values indicates falling to the left of the centroid). Notice that
most of their parent's s-Peaks statistics never got past the most
random and the noisiest regimes of the TD three (3) to five (5)
years old.
[0172] Herein, a new data type (s-Peaks) derived from raw kinematic
measurements was introduced with new statistical metrics
characterizing movement signatures at millisecond time scales. A
methodology is provided to characterize the micro-dynamics of
various aspects of the sensory motor physiology underlying natural
behaviors. Although a cohort of individuals with ASD was used to
instantiate the methods and the data type, the same framework can
be applied to a broad range of neurological disorders, particularly
those that are today diagnosed by observation and subjective
inventories.
[0173] The s-Peaks data revealed here captures the internally
generated physiological signals at the periphery directly extracted
from the output of high precision wearable sensors physically
attached to the body. By analogy, the wearable sensors can operate
in similar way as peripheral EEG sensors reading out activity from
large number of motor and sensory nerve ensembles. This is in
contrast to motions analyzed using camera-based systems, positioned
externally to the physical body. The kinematics signal derived from
camera-based systems rely on hand coding and learning algorithm
procedures (e.g., Moeslund et al. (2006) Computer Vision and Image
Understanding, 104:90-126) used to computerize observed behaviors.
In such cases a great deal of human heuristics and decision making
interventions are required to parse out, code and classify
behavioral information through learning algorithms that require
training and testing. During those processes of hand coding
behaviors the human eye inevitably may miss subtle information due
to fatigue, confirmation biases and limited detection capacity.
[0174] The metrics provided herein yield a different way to
phenotype disorders. In the cohort with ASD discussed here the
biometrics not only unambiguously distinguished ASD from typical
controls, but also found ASD subtyping that strongly correlated
with spoken language abilities. Previous studies have not separated
such levels of severity using clinical inventories (Hilton et al.
(2007) Res. Autism Spectrum Dis., 1:164-173; Hilton et al. (2011)
Autism:1362361311423018). Here, the importance of quantitatively
examining sensory-motor problems in autistic and other neurological
disorders has been strengthened.
[0175] This form of random and noisy action tremor found here
across subjects and trending with spoken language is very
intriguing because it is also present in most of the parents that
were examined from a random draw. The results presented here shed
light on relations between movements as a form of active senging
(kinesthetic sensory feedback) and the scaffolding of cognitive
abilities (specifically, spoken abilities).
[0176] Lastly, the methods presented here can also be used in
animal research that currently describes behaviors by observation.
Animal models of autism and other neurological disorders are based
on observation and description of the phenotype that genetic
manipulations may give rise to. By employing the objective
behavioral phenotyping provided herein, behavioral neuroscience and
genetics can be bridged so as to design target therapies for autism
and other disorders of the nervous system.
[0177] Methods
[0178] The subjects performed a basic pointing task paradigm as
illustrated in FIG. 13A. They comfortably sat down in front of a
touch screen. They were instructed to point to the target in the
center of the screen but were not instructed to retract the hand.
The subject spontaneously chose any motion after the touch. They
moved at their own comfortable pace. The target disappeared when
touched and reappeared later to initiate next reach. Their hand
motions were continuously captured at two hundred forty (240) Hz
sampling resolution (Polhemus Liberty, Colchester, Vt.).
[0179] 1. Speed Profiles and Smoothing Data Approach
[0180] FIGS. 19A-19B show the step by step procedure followed to
obtain the speed profiles. Target touch points are located as peaks
along the Y-axis (FIG. 19B). Hand movement velocity for each
direction was calculated as the first time derivative of the
position and smoothed out by the triangular smoothing algorithm
with bin size of twenty five (25) frames/one hundred four (104) ms
(FIGS. 19C-19D). The triangular smoothing procedure preserves the
positions of the peaks along the x-axis. This is important for the
temporal analyses on synchronicity and periodicity that was
performed on the empirical data. The speed profiles were obtained
from the square root of the sum of squares of the three velocity
components along the X-Y-Z orthogonal axes. s-Peaks were termed the
local millisecond range peak fluctuations within the temporal speed
profile.
[0181] FIG. 19 provides the rational of the triangular smoothing
algorithm. Often an un-weighted sliding-average (e.g. rectangular)
is used as a smoothing algorithm to filter out high frequency
noise. That algorithm simply replaces each point in the signal with
an average of n-adjacent points, where n is a positive integer
called the smoothening width. That way of averaging washes out
potentially important temporal information from the original raw
data set. It is clear that it affects the millisecond peak's
locations along the x-axis. But that is precisely the temporal
dynamics that is being analyzing. The triangular smoothing method,
however, does allow one to preserve those peak locations in present
in the original speed profiles. The triangular smoothing algorithm
with bin size 2d+1 (d=12) was implemented using the following
moving triangular window (FIG. 20A):
v ' ( i ) = k = - d d ( v ( k + i ) ( d + 1 - k ) ) k = - d d ( d -
1 - k ) ##EQU00005##
Here, v(i) is the i.sup.th element of the original speed profile
and v'(i) is the i.sup.th element of the smoothed profile: k is the
summation index, going from d to d. In this case, the total number
of elements is twenty five (25) with width d=12, centered at
element 13 and running from d=-12 (element 1 in the sliding window)
to d=12 (element 25 in the window). This builds up a symmetric
weighted sum around the central point.
[0182] FIGS. 20B-20C compare the outcomes from using the two
alternative smoothing methods. Note that the triangular smoothing
approach better preserves the peak shape and structure of the
profile, especially the peak's locations which play an essential
role in the temporal series analysis.
[0183] The s-Spike's dynamic analysis depends on the specific
optimal choice for smoothing bin size. Multiple possible scenarios
were numerical simulated to ascertain the robustness of the methods
used here and the stability of the results obtained for a range of
bin width values.
[0184] FIGS. 20D-20E show the results dependence on the choice of
smoothing bin width for the s-Peaks temporal analysis. To examine
the stability of the results, the mean p-IPI was calculated and the
R metric was defined from the p-IPI distribution (FIG. 16) using
different triangular smoothing bin widths. The simulations showed
that the subject's separation into different subtypes remained
stable for a range of bin widths (before the R values saturate).
The twenty five (25) frames bin width was selected such that the
separation among subtypes was clear and R was not yet saturated for
the control subjects.
[0185] The effects of taking the 25-frames-triangular-smoothing
algorithm on the speed profiles are shown in FIG. 20F. One line is
the speed calculated from the raw velocity's data and the other
line is the speed calculated from the smoothed velocities. The
algorithm gets rid of very high frequency fluctuations while
retaining the s-Peaks within the milliseconds time range.
[0186] 2. Definitions of the s-Peaks Vector and s-Peaks Matrix
[0187] An s-Peaks vector includes the full forward-and-backward
motion cycle. The s-Peaks matrix is built from these s-Peaks
vectors aligned along the touch. They were defined to capture the
s-Peaks temporal information cycle by cycle, and relative to the
touching point in each cycle (the intended goal of the task.). The
i.sup.th s-Peaks trial vector is defined as:
s i ( j ) = { 1 , s - Spike occurs at j th sampling point 0 , no s
- Spike at j th sampling point , ( j = 1 , 2 , , N j ) for i th
cycle ##EQU00006##
[0188] The s-Peaks matrix is then M(i,j)=s.sub.1(j) (i=1, 2, . . .
, N.sub.i; j=1, 2, . . . , N.sub.j). N.sub.i is the number of full
forward and back cycles (at least 100 cycles). N.sub.j is the
number of frames in each trial vector. A traditional rater gram
representation was used to visualize the s-Peaks cycle vector (FIG.
14A) and s-Spike matrix in (FIGS. 14B-14D). N.sub.j is set as eight
hundred (800) (-3.4 s), with four hundred (400) frames (-1.7 s)
before and after the touching point for all subjects. All s-Peaks
vectors for one experimental session are given by the rows of the
s-Peaks matrix M. FIG. 14A provides a way to visualize the s-Peaks
vectors and the s-Peaks matrices (FIGS. 14B-14D) across
representative subjects. Each dot represents a s-Spike. The matrix
provides the s-Peaks rastergram. The s-Peaks-Spikes' mean `firing
rate` across trials was also calculated every twenty (20) frames
and shown in the figure. The s-Peaks vectors were chopped into
successive bins (named as s-Peaks cycle chopped vectors) in some of
the following analyses. In the case of the s-Peaks cycle chopped
vectors, is the s-Peaks cycle chopped vectors sT for i.sup.th cycle
with chopped bin size equal to r. If any s-Peaks appear in the
k.sup.th bin (number of s-Peaks inside the kth bin>0) s; (k)=1:
Otherwise, sT (k)=0.
[0189] 3. Numerical Simulation and Analytical Analysis of the
Population Cross-Correlation Function
[0190] For a homogeneous Poison random spike train, the possibility
for each sample data point to have a spike equals `r`. For two
uncorrelated spike trains (S.sub.i, S.sub.j) of length N and firing
rate r, the cross-correlation of the two trains is zero. If one
adds one zero in the same position in both trains, the cross
correlation function can be calculated from its definition:
C i , j = l = 1 N + 1 ( S i ( l ) - S i _ ) ( S j ( l ) - S j _ ) l
= 1 N + 1 ( S i ( l ) - S i _ ) 2 ( S i ( l ) - S i _ ) 2
##EQU00007## with S i ( N + 1 ) = S j ( N + 1 ) = 0 , S i _ = S j _
= rN N + 1 .about. r , N 1. ##EQU00007.2##
[0191] The two trains are thus not correlated and the probability
for each element (element 1 to N) to be one (1) is r
(p.sub.i(1)=p.sub.j(1)=r). The probability for one (1) pair of
elements to be (S.sub.i(l),S.sub.j(l))=(1,1) is
P(1,1)=p.sub.i(1)=p.sub.j(1)=r.sup.2. Similarly,
P(0,1)=P(1,0)=r(1-r) and P(0,0)=(1-r).sup.2. Hence, within N sample
pairs, the number of pairs (1, 1), (0, 1), (1, 0), (0, 0) would be
r.sup.2N, r(1-r)N, r(1-r)N, (1-r).sup.2N respectively. It can be
shown that
l = 1 N ( S i ( l ) - S i _ ) ( S j ( l ) - S j _ ) = 0.
##EQU00008##
The correlation equation numerator can then be written as
l = 1 N + 1 ( S i ( l ) - S i _ ) ( S j ( l ) - S j _ ) = l = 1 N (
S i ( l ) - S i _ ) ( S j ( l ) - S j _ ) + r 2 = r 2 . Furthermore
, l = 1 N ( S i ( l ) - S i _ ) 2 = l = 1 N + 1 ( S j ( l ) - S j _
) 2 = r ( 1 - r ) N + r 2 . ##EQU00009##
The denominator equals
l = 1 N + 1 ( S i ( l ) - S i _ ) 2 l = 1 N + 1 ( S j ( l ) - S j _
) 2 = r ( 1 - r ) N + r 2 . Hence , C i , j = r ( 1 - r ) N + r
##EQU00010##
For spike trains with correlations,
l = 1 N ( S i ( l ) - S i _ ) ( S j ( l ) - S j _ ) > 0 , Then
##EQU00011## C i , j > r ( 1 - r ) N + r . ##EQU00011.2##
For a spike train of length N.sub.o and firing rate r.sub.o chopped
into successive bins with bin width .tau., the chopped vector will
have the length
N .tau. = N .tau. . ##EQU00012##
Each bin is assigned as one when there is at least one s-Peaks in
the bin and zero otherwise. The probability of having one for each
element is r.sub..tau.=1-(1-r.sub.0).sup.r.
[0192] Together with the equations derived above, the population
cross-correlation function among the uncorrelated poison spike
trains chopped into bins with length
.tau. (with 0 added at the end of each train) is
C r ( .tau. ) = r .tau. ( 1 - r .tau. ) N .tau. + r .tau. , N .tau.
= N .tau. , r .tau. = 1 - ( 1 - r .tau. ) .tau. . ##EQU00013##
The curve increases from zero (0) to one (1) with a positive second
derivative. Since the firing rate r is small, C.sub.r(.tau.) can be
approximated as r.tau..sup.2/N.
[0193] For synchronized spike trains, for .tau.=1,
C(.tau.).about.C.sub.r(.tau.); when .tau..fwdarw..infin.,
C(.tau.)=C.sub.r(.tau.)=1 and C(.tau.)>C.sub.r(.tau.) for a.tau.
value in between. Hence, the second derivative of the C(.tau.)
curve for synchronized spike trains will be smaller than that for
unsynchronized ones: C(.tau.) grows faster at lower .tau. values
and slower at larger .tau. when compared to the behavior for
totally random trains (C.sub.r(.tau.)). Based on this analysis, the
synchronicity of the spike trains have been quantified using the
second derivative of C(.tau.) and its deviation (distance) from the
simulated curve for total random trains (C.sub.r(.tau.)).
[0194] In FIG. 20, the results for a numerically generated Poison
random spike trains and synchronized spike trains are shown. The
homogenous Poison spike train was generated with firing rate equal
to r/sampling point: r for each trial is random
numbers generated between zero (0) and one tenth (0.1) (average
value r.sub.o=0.05). Each trial has five hundred (500) sampling
points with three hundred (300) generated trials. The synchronized
spike train was generated with a time dependent firing rate. Within
each trial, the firing rate is
r=cos(4.pi.t/500)r.sub.0+r.sub.0,
with mean firing rate equal to
{tilde over (r)}=r.sub.0=0.05
FIGS. 20A-20B show the rastergram for both cases.
[0195] The p-IPI-like distribution is plotted in FIGS. 20C-20D. A
good exponential fit on the IPIs was found below forty (40) with
the residual IPI distribution shown in the bottom panels. The
synchronized spike train shows a hump around IPI=400 outside the
exponential fit region, similar to the control subject case shown
in FIG. 15C.
[0196] The random spike train exhibits a single exponential
distribution, as found in the low functioning ASD case shown in
FIG. 15A. The cross-correlation measure analysis, as applied to the
empirical data, also applies to the numerically generated spike
trains (ranging from totally random to partly synchronized spike
trains). FIG. 20E shows the difference between the C(.tau.) curves
in the two extreme limiting cases: the random spike train has a
C(.tau.) with an increasing slope at small r while the synchrony
spike train case shows a decreasing slope (shown in the insets).
The curve for random spike train agrees well with the analytical
result (dashed line).
[0197] 4. Population Cross-Correlation s-Peaks Matrix Analysis
[0198] s-Peaks cycle cross-correlations for any two pairs of
chopped vectors were calculated from,
C i , j ( .tau. ) = l = 1 N + 1 ( S i .tau. ( l ) - S i .tau. _ ) (
S j .tau. ( l ) - S j .tau. _ ) l = 1 N + 1 ( S i .tau. ( l ) - S i
.tau. _ ) 2 l = 1 N + 1 ( S i .tau. ( l ) - S i .tau. _ ) 2
##EQU00014##
s.sub.i.sup..tau.(s.sub.j.sup..tau.) is the i.sup.th (j.sup.th).
s-Peaks cycle chopped vector (chopped bin size equal .tau.), with a
zero (0) added at the end of the vector,
s.sub.i.sup..tau.(s.sub.j.sup..tau.) is the mean value of the
i.sup.th(j.sup.th) s-Peaks cycle chopped vector.
[0199] Averaging c.sub.i,j(.tau.) across all cycle pairs (all i, j
for i.noteq.j) provides the population cross-correlation function
C(.tau.) of the process. C(.tau.) was calculated for various values
of .tau. across subjects. As demonstrated in the following section,
the shape of the C(.tau.) curve as a function of .tau., especially
its second derivative can be related to the s-Peaks synchronicity
across repetitions. Based on this, the C(.tau.) curve was fitted
with a quadratic polynomial function
f(.tau.)+p.sub.1.tau..sup.2+p.sub.3 and the degree of synchronicity
was determined via the fitted second derivative p.sub.1.
[0200] The shape of the C(.tau.) curve also depends on conditions
other than synchronicity, like the s-Peaks firing rate and cycle
length. To address this question, a total random poison spike train
process was simulated with the same firing rate and trial length
for each subject calculating the distance from the empirical
C(.tau.) the total randomness C.sub.r(.tau.) curve. The curves'
separation distance was defined as the maximum separation of the
two curves (considering the region between one (1) sample point
(0.004 s) to seventy (70) sample points (0.3 s)).
[0201] Simulating the total random process (homogeneous Poison
process) and a partially synchronous process helped set proper
analytical bounds to better understand the empirical data. The
C(.tau.) obtained for the homogeneous Poison random process agrees
well with the analytically calculated curve for total random spike
train (C.sub.r(.tau.)), i.e. increasing slowly for small bin size
and the growth rate increases as .tau. increasing (positive second
derivative). At the other extreme, the simulated C(.tau.) for the
partially synchronous spike train increases faster for small bin
size with a growth rate decreasing as .tau. increases (negative
second derivative), clearly deviating from the random process
curve. Based on these simulated results, the s-Peaks synchronicity
was interpreted in terms of the second derivative of the C(.tau.)
curve and its distance to the C.sub.r(.tau.)curve. Second
derivative was fitted with the curve before the population
cross-correlation value reach six tenths (0.6) and with .tau.
smaller than fifty (50) frames. The relationship between s-Peaks
synchronization and the level of spoken verbal abilities was
investigated (FIG. 15).
[0202] 5. FFT Analysis of Trajectories and s-Peaks Chopped
Vectors
[0203] In addition to studying the s-Spike's synchronicity across
repetitions (cycles), s-Peaks periodicities along the whole
continuous movement profiles were considered by calculating
autocorrelations of s-Peaks' occurrences in the full speed profile
(FIG. 22). An s-Peaks full-profile vector includes s-Peaks in the
whole speed profile continuously. By chopping the s-Peaks
full-profile vector into successive bins with bin width size
.tau.=24 frames (100 ms), the s-Peaks full profile chopped vector
(S.sup..tau.(l)) was obtained.
[0204] The (unbiased) autocorrelation function was calculated
by
R ( m ) = m = 0 N s - m - t S .tau. ( n + m ) S .tau. ( n ) N s - m
. ##EQU00015##
Here N, is the total bin number in the vector; m is the time lag
varying from 0 to seven thousand two hundred (7,200) frames/three
hundred (300) bins/thirty (30) seconds. Maximum time lag of thirty
(30) seconds is limited by the thirsty (30) second-time window of
the data cycles.
[0205] The periodicity was also checked using a power spectrum
analysis. For a given output X, including both signal and noise,
the power spectrum of the pure signal can be calculated from the
FFT of the autocorrelation function (Wiener-Khinchin theorem;
Wiener, N. (1930) Acta mathematica 55:117-258):
PW.sub.s(w)=|F.sub.s(w)|.sup.2=FR(w)/ {square root over
(2.pi.)},
[0206] Where PW.sub.s(w) is the power spectrum of the pure signal
with FR(w) the Fourier transform amplitude of the signal
autocorrelation and F.sub.S(w) is the Fourier transform amplitude
of the pure signal. FIGS. 22D-22F shows the s-Peaks-Spikes' power
spectra for the three (3) representative subjects calculated from
the s-Peaks full-profile chopped vectors' (bin width size r=24
frames) autocorrelations, with time lag from zero (0) to thirsty
(30) seconds, as well as the power spectra calculated directly from
the FFT of the spike train. As shown in the figure the magnitude of
the autocorrelation FFT gives a smoothed version of the power
spectrum of the spike trains.
[0207] 6. Distribution Analysis of s-Peaks Time Intervals (the
s-IPIs)
[0208] The s-IPIs is defined as the time intervals between
nearest-neighbor s-Peaks. The distribution of s-IPIs during the
reaching and retracting periods (kinetic s-IPIs) was constructed
for each subject in the cohort. As shown in FIGS. 17A-17C, small
s-IPIs (below 10 frames/40 ms) was exponentially distributed.
Exponentially distributed intervals correspond to randomly
scattered s-Spikes separations (Ross, S. M. (1987) Introduction to
probability and statistics for engineers and scientists. New York,
N.Y.: Wiley) and hence the remaining s-IPIs values falling outside
of the exponential fit contained important information away from
full randomness.
[0209] The R metric introduced here quantifies the proportion of
s-IPI outliers (durations) from the exponential distribution: If
R=0, all s-IPIs are exponentially distributed, suggesting total
randomness of the s-Spikes occurrences. Otherwise, having a larger
proportion of outliers corresponds to further deviation from total
randomness. However, the unbiased proportion of s-IPIs cannot
distinguish, for example, one distribution with two small outlier
intervals of length t from one with single outlier interval of
length 2t. The speed profile of the latter case is actually
smoother, or less random. Based on this, R is defined as:
R = j n j , out .times. pISI j 2 i n i .times. pISI i ( j > 10 )
. ##EQU00016##
[0210] Here pIPI.sub.i is the s-IPI interval at the i.sup.th bin,
where the bin width is two (2) frames (.about.8 ms). n.sub.i is the
s-IPIs count in the i.sup.th bin; n.sub.j, out is the s-IPIs count
away from the exponential fit. In cases where there is not enough
small s-IPIs for a good exponential fit or the speed profile is
very smooth with rare small s-IPIs present, n.sub.j, out=n.sub.j. R
has the same dimension as s-IPI (ms in this case). Notice that R is
normalized, so as to be independent of the number of trials
included in the distribution.
[0211] A parameter plane with R was built as a vertical axis and
the mean kinetic s-IPI value as its horizontal axis (FIG. 17D).
Each subject (across both ASD and TD groups) was located as a point
in the phase diagram. The points in the plane are separated into
separated clusters based on K-means cluster analysis (Theodoridis,
S. (2010) Introduction to pattern recognition: a MATLAB approach.
Burlington, Mass.: Academic Press). The positions (the cluster
indexes) of the subjects were compared to their clinical diagnoses.
This parameter plane analysis was further extended to the
discussion of age development and potential parental link.
[0212] 7. Power Spectrum Analysis of s-Peaks Vectors
[0213] FIGS. 21A-21C show the autocorrelation of s-Peaks chopped
vectors for three (3) sample subjects (LF ASD, HF ASD, control) as
in FIG. 16B. FIGS. 22D-22E show the power spectrum of the s-Peaks
chopped vectors (as in FIG. 15I). The lines are the power spectrum
calculated directly from the FFT of the spike train and the coded
thicker lines are the FFT from the autocorrelation function. As
shown in the figure the magnitude of the autocorrelation FFT gives
a smoothed version of the power spectrum of the spike trains. FIGS.
22D-22E clearly show the periodicity difference of the s-Peaks
occurrences across three representative subjects.
[0214] While certain of the preferred embodiments of the present
invention have been described and specifically exemplified above,
it is not intended that the invention be limited to such
embodiments. Various modifications may be made thereto without
departing from the scope and spirit of the present invention, as
set forth in the following claims.
[0215] All of the apparatus, methods, and algorithms disclosed and
claimed herein can be made and executed without undue
experimentation in light of the present disclosure. While the
invention has been described in terms of preferred embodiments, it
will be apparent to those having ordinary skill in the art that
variations may be applied to the apparatus, methods and sequence of
steps of the method without departing from the concept, spirit and
scope of the invention. More specifically, it will be apparent that
certain components may be added to, combined with, or substituted
for the components described herein while the same or similar
results would be achieved. All such similar substitutes and
modifications apparent to those having ordinary skill in the art
are deemed to be within the spirit, scope and concept of the
invention as defined.
[0216] The features and functions disclosed above, as well as
alternatives, may be combined into many other different systems or
applications. Various presently unforeseen or unanticipated
alternatives, modifications, variations or improvements may be made
by those skilled in the art, each of which is also intended to be
encompassed by the disclosed embodiments.
* * * * *